APPLICATION FOR UNITED STATES PATENT
`
`INVENTORS:
`
`ALAN J. LIPTON
`THOMAS M. STRAT
`PETER L. VENETIANER
`MARK C. ALLMEN
`WILLIAM E. SEVERSON
`NIELS HAERING
`ANDREW J. CHOSAK
`ZHONG ZHANG
`MATTHEW F. FRAZIER
`JAMES S. SFEK.AS
`TASUK! HIRATA
`JOHN CLARK
`
`TITLE:
`
`VIDEO SURVEILLANCE SYSTEM EMPLOYING
`VIDEO PRIMITNES
`
`ATTORNEYS' ADDRESS:
`
`VENABLE
`1201 New York Avenue, N.W., Suite 1000
`Washington, D.C. 20005-3917
`Telephone: (202) 962-4800
`Telefax: (202) 962-8300
`
`ADDRESS FOR U.S.P.T.O. CORRESPONDENCE:
`
`VENABLE
`Post Office Box 34385
`Washington, D.C. 20043-9998
`
`ATTORNEY DOCKET NO.:
`
`37112-175340
`
`

`

`VIDEO SURVEILLANCE SYSTEM EMPLOYING VIDEO PRIMITIVES
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`CROSS-REFERENCE TO RELATED APPLICATIONS
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`[1]
`
`This application claims the priority of U.S. Patent Application No. 09/694,712
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`5
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`filed October 24, 2000, which is incorporated herein by reference.
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`BACKGROUND OF THE INVENTION
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`Field of the Invention
`
`[2]
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`The invention relates to a system for automatic video surveillance employing
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`~ video primitives.
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`References
`
`[3]
`
`For the convenience of the reader, the references referred to herein are listed
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`below. In the specification, the numerals within brackets refer to respective references. The
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`!il5
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`listed references are incorporated herein by reference.
`
`[4]
`
`[5]
`
`The following references describe moving target detection:
`
`{1} A. Lipton, H. Fujiyoshi and R. S. Patil, "Moving Target Detection and
`
`Classification from Real-Time Video," Proceedings ofiEEE WACV '98, Princeton, NJ, 1998,
`
`pp. 8-14.
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`20
`
`[6]
`
`{2} W.E.L. Grimson, et al., ''Using Adaptive Tracking to Classify and Monitor
`
`Activities in a Site", CVPR, pp. 22-29, June 1998.
`
`[7]
`
`{3} A.J. Lipton, H. Fujiyoshi, R.S. Patil, "Moving Target Classification and
`
`Tracking from Real-time Video," IUW, pp. 129-136, 1998.
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`[8]
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`{ 4} T .J. Olson and F .Z. Brill, "Moving Object Detection and Event Recognition
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`Algorithm for Smart Cameras," IUW, pp. 159-175, May 1997.
`
`[9]
`
`The following references describe detecting and tracking humans:
`
`[10]
`
`{5} A. J. Lipton, "Local Application of Optical Flow to Analyse Rigid Versus
`
`5 Non-Rigid Motion," International Conference on Computer Vision, Corfu, Greece, September
`
`1999.
`
`[11]
`
`{ 6} F. Bartolini, V. Cappellini, and A. Mecocci, "Counting people getting in and
`
`out of a bus by real-time image-sequence processing," IVC, 12(1):36-41, January 1994.
`
`[12]
`
`{7} M. Rossi and A. Bozzoli, "Tracking and counting moving people," ICIP94,
`
`[13]
`
`{8} C.R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, "Pfinder: Real-
`
`time tracking of the human body," Vismod, 1995.
`
`[14]
`
`{9} L. Khoudour, L. Duvieubourg, J.P. Deparis, "Real-Time Pedestrian Counting
`
`by Active Linear Cameras," JEI, 5(4):452-459, October 1996.
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`15
`
`[15]
`
`{10} S. loffe, D.A. Forsyth, ~'Probabilistic Methods for Finding People," IJCV,
`
`43(1):45-68, June 2001.
`
`[16]
`
`{ 11} M. Isard and J. MacCormick, "BraMBLe: A Bayesian Multiple-Blob
`
`Tracker," ICCV, 2001.
`
`[17] The following references describe blob analysis:
`
`20
`
`(18]
`
`{12} D.M. Gavrila, "The Visual Analysis of Human Movement: A Survey,"
`
`CVIU, 73(1):82-98, January 1999.
`
`[19]
`
`{ 13} Niels Haering and Niels da Vitoria Lobo, "Visual Event Detection," Video
`
`Computing Series, Editor Mubarak Shah, 2001.
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`[20] The following references describe blob analysis for trucks, cars, and people:
`
`[21]
`
`{14} Collins, Lipton, Kanade, Fujiyoshi, Duggins, Tsin, Tolliver, Enomoto, and
`
`Hasegawa, "A System for Video Surveillance and Monitoring: VSAM Final Report," Technical
`
`Report CMU-Rl-TR-00-12, Robotics Institute, Carnegie Mellon University, May 2000.
`
`5
`
`[22]
`
`{15} Lipton, Fujiyoshi, and Patil, "Moving Target Classification and Tracking
`
`from Real-time Video," 98 Darpa IUW, Nov. 20-23, 1998.
`
`[23] The following reference describes analyzing a single-person blob and its contours:
`
`[24]
`
`{16} C.R. Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland. "Pfinder: Real-
`
`Time Tracking of the Human Body," PAMI, vol19, pp. 780-784, 1997.
`
`{~
`
`[25] The following reference describes internal motion of blobs, including any motion-
`
`based segmentation:
`
`[26]
`
`{17} M. Allmen and C. Dyer, "Long--Range Spatiotemporal Motion
`
`Understanding Using Spatiotemporal Flow Curves," Proc. IEEE CVPR, Lahaina, Maui, Hawaii,
`
`pp. 303-309, 1991.
`
`15
`
`[27]
`
`{18} L. Wixson, "Detecting Salient Motion by Accumulating Directionally
`
`Consistent Flow", IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, pp. 774-781, Aug, 2000.
`
`Background of the Invention
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`[28] Video surveillance of public spaces has become extremely widespread and
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`20
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`accepted by the general public. Unfortunately, conventional video surveillance systems produce
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`such prodigious volumes of data that an intractable problem results in the analysis of video
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`surveillance data.
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`[29] A need exists to reduce the amount of video surveillance data so analysis ofthe
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`video surveillance data can be conducted.
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`[30] A need exists to filter video surveillance data to identify desired portions of the
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`video surveillance data.
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`5
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`'rb
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`~~~:o~
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`SUMMARY OF THE INVENTION
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`[31] An object of the invention is to reduce the amount of video surveillance data so
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`analysis of the video surveillance data can be conducted.
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`[32] An object of the invention is to filter video surveillance data to identify desired
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`portions of the video surveillance data.
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`[33] An object of the invention is to produce a real time alarm based on an automatic
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`detection of an event from video surveillance data.
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`[34] An object of the invention is to integrate data from surveillance sensors other than
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`video for improved searching capabilities.
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`15
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`[35] An object of the invention is to integrate data from surveillance sensors other than
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`video for improved event detection capabilities
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`[36] The invention includes an article of manufacture, a method, a system, and an
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`apparatus for video surveillance.
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`[37]
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`The article of manufacture of the invention includes a computer-readable medium
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`20
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`comprising software for a video surveillance system, comprising code segments for operating the
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`video surveillance system based on video primitives.
`
`[38]
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`The article of manufacture of the invention includes a computer-readable medium
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`comprising software for a video surveillance system, comprising code segments for accessing
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`archived video primitives, and code segments for extracting event occurrences from accessed
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`archived video primitives.
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`[39] The system of the invention includes a computer system including a computer-
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`readable medium having software to operate a computer in accordance with the invention.
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`5
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`[ 40] The apparatus of the invention includes a computer including a computer-readable
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`medium having software to operate the computer in accordance with the invention.
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`[41] The article of manufacture ofthe invention includes a computer-readable medium
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`having software to operate a computer in accordance with the invention.
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`[ 42] Moreover, the above objects and advantages of the invention are illustrative, and
`
`not exhaustive, of those that can be achieved by the invention. Thus, these and other objects and
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`advantages of the invention will be apparent from the description herein, both as embodied
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`"'
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`herein and as modified in view of any variations which will be apparent to those skilled in the
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`art.
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`l5 Definitions
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`[43] A "video" refers to motion pictures represented in analog and/or digital form.
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`Examples of video include: television, movies, image sequences from a video camera or other
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`observer, and computer-generated image sequences.
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`[44] A "frame" refers to a particular image or other discrete unit within a video.
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`20
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`[ 45] An "object" refers to an item of interest in a video. Examples of an object
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`include: a person, a vehicle, an animal, and a physical subject.
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`[46] An "activity" refers to one or more actions and/or one or more composites of
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`actions of one or more objects. Examples of an activity include: entering; exiting; stopping;
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`moving; raising; lowering; growing; and shrinking.
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`[ 4 7] A "location" refers to a space where an activity may occur. A location can be, for
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`5
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`example, scene-based or image-based. Examples of a scene-based location include: a public
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`space; a store; a retail space; an office; a warehouse; a hotel room; a hotel lobby; a lobby of a
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`building; a casino; a bus station; a train station; an airport; a port; a bus; a train; an airplane; and
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`a ship. Examples of an image-based location include: a video image; a line in a video image; an
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`area in a video image; a rectangular section of a video image; and a polygonal section of a video
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`l-<9
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`image.
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`[48] An "event" refers to one or more objects engaged in an activity. The event may
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`be referenced with respect to a location and/or a time.
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`[ 49] A "computer" refers to any apparatus that is capable of accepting a structured
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`input, processing the structured input according to prescribed rules, and producing results of the
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`15
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`processing as output. Examples of a computer include: a computer; a general purpose computer;
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`a supercomputer; a mainframe; a super mini-computer; a mini-computer; a workstation; a micro(cid:173)
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`computer; a server; an interactive television; a hybrid combination of a computer and an
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`interactive television; and application-specific hardware to emulate a computer and/or software.
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`A computer can have a single processor or multiple processors, which can operate in parallel
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`20
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`and/or not in parallel. A computer also refers to two or more computers connected together via a
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`network for transmitting or receiving information between the computers. An example of such a
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`computer includes a distributed computer system for processing information via computers
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`linked by a network.
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`[50] A "computer-readable medium" refers to any storage device used for storing data
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`accessible by a computer. Examples of a computer-readable medium include: a magnetic hard
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`disk; a floppy disk; an optical disk, such as a CD-ROM and a DVD; a magnetic tape; a memory
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`chip; and a carrier wave used to carry computer-readable electronic data, such as those used in
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`5
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`transmitting and receiving e-mail or in accessing a network.
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`[51]
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`"Software" refers to prescribed rules to operate a computer. Examples of
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`software include: software; code segments; instructions; computer programs; and programmed
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`logic.
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`[52] A "computer system" refers to a system having a computer, where the computer
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`comprises a computer-readable medium embodying software to operate the computer.
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`[53] A "network" refers to a number of computers and associated devices that are
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`"'
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`connected by communication facilities. A network involves permanent connections such as
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`cables or temporary connections such as those made through telephone or other communication
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`links. Examples of a network include: an internet, such as the Internet; an intranet; a local area
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`~t5
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`network (LAN); a wide area network (WAN); and a combination of networks, such as an internet
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`and an intranet.
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`BRIEF DESCRIPTION OF THE DRAWINGS
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`[54] Embodiments ofthe invention are explained in greater detail by way of the
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`20
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`drawings, where the same reference numerals refer to the same features.
`
`[55]
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`Figure 1 illustrates a plan view of the video surveillance system of the invention.
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`[56]
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`Figure 2 illustrates a flow diagram for the video surveillance system of the
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`invention.
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`[57]
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`Figure 3 illustrates a flow diagram for tasking the video surveillance system.
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`[58]
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`Figure 4 illustrates a flow diagram for operating the video surveillance system.
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`[59]
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`Figure 5 illustrates a flow diagram for extracting video primitives for the video
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`surveillance system.
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`5
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`[60]
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`Figure 6 illustrates a flow diagram for taking action with the video surveillance
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`system.
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`[61]
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`Figure 7 illustrates a flow diagram for semi-automatic calibration of the video
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`surveillance system.
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`[62]
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`Figure 8 illustrates a flow diagram for automatic calibration of the video
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`f~
`. ')~.:.:;~g
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`surveillance system .
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`[63]
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`Figure 9 illustrates an additional flow diagram for the video surveillance system
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`of the invention.
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`[64]
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`Figures 10-15 illustrate examples of the video surveillance system of the
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`invention applied to monitoring a grocery store.
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`15
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`DETAILED DESCRIPTION OF THE INVENTION
`
`[ 65]
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`The automatic video surveillance system of the invention is for monitoring a
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`location for, for example, market research or security purposes. The system can be a dedicated
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`video surveillance installation with purpose-built surveillance components, or the system can be
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`20
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`a retrofit to existing video surveillance equipment that piggybacks off the surveillance video
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`feeds. The system is capable of analyzing video data from live sources or from recorded media.
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`The system can have a prescribed response to the analysis, such as record data, activate an alarm
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`mechanism, or active another sensor system. The system is also capable of integrating with
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`other surveillance system components. The system produces security or market research reports
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`that can be tailored according to the needs of an operator and, as an option, can be presented
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`through an interactive web-based interface, or other reporting mechanism.
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`[ 66] An operator is provided with maximum flexibility in configuring the system by
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`5
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`using event discriminators. Event discriminators are identified with one or more objects (whose
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`descriptions are based on video primitives), along with one or more optional spatial attributes,
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`and/or one or more optional temporal attributes. For example, an operator can define an event
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`discriminator (called a "loitering" event in this example) as a "person" object in the "automatic
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`teller machine" space for "longer than 15 minutes" and "between 10:00 p.m. and 6:00 a.m."
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`[67] Although the video surveillance system of the invention draws on well-known
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`computer vision techniques from the public domain, the inventive video surveillance system has
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`several unique and novel features that are not currently available. For example, current video
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`surveillance systems use large volumes of video imagery as the primary commodity of
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`information interchange. The system of the invention uses video primitives as the primary
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`15
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`commodity with representative video imagery being used as collateral evidence. The system of
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`the invention can also be calibrated (manually, semi-automatically, or automatically) and
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`thereafter automatically can infer video primitives from video imagery. The system can further
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`analyze previously processed video without needing to reprocess completely the video. By
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`analyzing previously processed video, the system can perform inference analysis based on
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`20
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`previously recorded video primitives, which greatly improves the analysis speed of the computer
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`system.
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`[68] As another example, the system of the invention provides unique system tasking.
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`Using equipment control directives, current video systems allow a user to position video sensors
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`and, in some sophisticated conventional systems, to mask out regions of interest or disinterest.
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`Equipment control directives are instructions to control the position, orientation, and focus of
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`video cameras. Instead of equipment control directives, the system of the invention uses event
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`discriminators based on video primitives as the primary tasking mechanism. With event
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`5
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`discriminators and video primitives, an operator is provided with a much more intuitive approach
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`over conventional systems for extracting useful information from the system. Rather than
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`tasking a system with an equipment control directives, such as "camera A pan 45 degrees to the
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`left," the system of the invention can be tasked in a human-intuitive manner with one or more
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`event discriminators based on video primitives, such as "a person enters restricted area A."
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`[69] Using the invention for market research, the following are examples of the type of
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`video surveillance that can be performed with the invention: counting people in a store; counting
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`people in a part of a store; counting people who stop in a particular place in a store; measuring
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`how long people spend in a store; measuring how long people spend in a part of a store; and
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`measuring the length of a line in a store.
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`15
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`[70] Using the invention for security, the following are examples of the type of video
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`surveillance that can be performed with the invention: determining when anyone enters a
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`restricted area and storing associated imagery; determining when a person enters an area at
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`unusual times; determining when changes to shelf space and storage space occur that might be
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`unauthorized; determining when passengers aboard an aircraft approach the cockpit; determining
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`20 when people tailgate through a secure portal; determining if there is an unattended bag in an
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`airport; and determining if there is a theft of an asset.
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`[71]
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`Figure 1 illustrates a plan view of the video surveillance system of the invention.
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`A computer system 11 comprises a computer 12 having a computer-readable medium 13
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`embodying software to operate the computer 12 according to the invention. The computer
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`system 11 is coupled to one or more video sensors 14, one or more video recorders 15, and one
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`or more input/output (I/0) devices 16. The video sensors 14 can also be optionally coupled to
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`the video recorders 15 for direct recording of video surveillance data. The computer system is
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`5
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`optionally coupled to other sensors 17.
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`[72]
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`The video sensors 14 provide source video to the computer system 11. Each
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`video sensor 14 can be coupled to the computer system 11 using, for example, a direct
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`connection (e.g., a firewire digital camera interface) or a network. The video sensors 14 can
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`exist prior to installation of the invention or can be installed as part of the invention. Examples
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`7~~=
`~l.5
`'lj)
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`of a video sensor 14 include: a video camera; a digital video camera; a color camera; a
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`monochrome camera; a camera; a camcorder, a PC camera; a webcam; an infra-red video
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`camera; and a CCTV camera.
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`[73] The video recorders 15 receive video surveillance data from the computer system
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`11 for recording and/ or provide source video to the computer system 11. Each video recorder 15
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`can be coupled to the computer system 11 using, for example, a direct connection or a network.
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`The video recorders 15 can exist prior to installation of the invention or can be installed as part
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`of the invention. Examples of a video recorder 15 include: a video tape recorder; a digital video
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`recorder; a video disk; a DVD; and a computer-readable medium.
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`[7 4]
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`The I/0 devices 16 provide input to and receive output from the computer system
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`20
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`11. The I/0 devices 16 can be used to task the computer system 11 and produce reports from the
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`computer system 11. Examples of I/0 devices 16 include: a keyboard; a mouse; a stylus; a
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`monitor; a printer; another computer system; a network; and an alarm.
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`[75] The other sensors 17 provide additional input to the computer system 11. Each
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`other sensor 17 can be coupled to the computer system 11 using, for example, a direct connection
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`or a network. The other sensors 17 can exit prior to installation of the invention or can be
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`installed as part of the invention. Examples of another sensor 17 include: a motion sensor; an
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`5
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`optical tripwire; a biometric sensor; and a card-based or keypad-based authorization system. The
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`outputs of the other sensors 17 can be recorded by the computer system 11, recording devices,
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`and/or recording systems.
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`(76]
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`Figure 2 illustrates a flow diagram for the video surveillance system of the
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`invention. Various aspects of the invention are exemplified with reference to Figures 10-15,
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`which illustrate examples of the video surveillance system of the invention applied to monitoring
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`a grocery store.
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`[77]
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`In block 21, the video surveillance system is set up as discussed for Figure 1.
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`Each video sensor 14 is orientated to a location for video surveillance. The computer system 11
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`is connected to the video feeds from the video equipment 14 and 15. The video surveillance
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`~f5
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`system can be implemented using existing equipment or newly installed equipment for the
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`location.
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`[78]
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`In block 22, the video surveillance system is calibrated. Once the video
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`surveillance system is in place from block 21, calibration occurs. The result of block 22 is the
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`ability of the video surveillance system to determine an approximate absolute size and speed of a
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`20
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`particular object (e.g., a person) at various places in the video image provided by the video
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`sensor. The system can be calibrated using manual calibration, semi-automatic calibration, and
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`automatic calibration. Calibration is further described after the discussion of block 24.
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`[79]
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`In block 23 of Figure 2, the video surveillance system is tasked. Tasking occurs
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`after calibration in block 22 and is optional. Tasking the video surveillance system involves
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`specifying one or more event discriminators. Without tasking, the video surveillance system
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`operates by detecting and archiving video primitives and associated video imagery without
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`5
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`taking any action, as in block 45 in Figure 4.
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`[80]
`
`Figure 3 illustrates a flow diagram for tasking the video surveillance system to
`
`determine event discriminators. An event discriminator refers to one or more objects optionally
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`interacting with one or more spatial attributes and/or one or more temporal attributes. An event
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`discriminator is described in terms of video primitives. A video primitive refers to an observable
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`attribute of an object viewed in a video feed. Examples of video primitives include the
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`following: a classification; a size; a shape; a color; a texture; a position; a velocity; a speed; an
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`internal motion; a motion; a salient motion; a feature of a salient motion; a scene change; a
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`feature of a scene change; and a pre-defined model.
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`[81] A classification refers to an identification of an object as belonging to a particular
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`15
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`category or class. Examples of a classification include: a person; a dog; a vehicle; a police car;
`
`an individual person; and a specific type of object.
`
`[82] A size refers to a dimensional attribute of an object. Examples of a size include:
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`large; medium; small; :flat; taller than 6 feet; shorter than 1 foot; wider than 3 feet; thinner than 4
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`feet; about human size; bigger than a human; smaller than a human; about the size of a car; a
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`20
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`rectangle in an image with approximate dimensions in pixels; and a number of image pixels.
`
`[83] A color refers to a chromatic attribute of an object. Examples of a color include:
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`white; black; grey; red; a range ofHSV values; a range ofYUV values; a range ofRGB values;
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`an average RGB value; an average YUV value; and a histogram ofRGB values.
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`[84] A texture refers to a pattern attribute of an object. Examples of texture features
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`include: self-similarity; spectral power; linearity; and coarseness.
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`[85] An internal motion refers to a measure of the rigidity of an object. An example of
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`a fairly rigid object is a car, which does not exhibit a great amount of internal motion. An
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`5
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`example of a fairly non-rigid object is a person having swinging arms and legs, which exhibits a
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`great amount of internal motion.
`
`[86] A motion refers to any motion that can be automatically detected. Examples of a
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`motion include: appearance of an object; disappearance of an object; a vertical movement of an
`
`object; a horizontal movement of an object; and a periodic movement of an object.
`
`[87] A salient motion refers to any motion that can be automatically detected and can
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`be tracked for some period of time. Such a moving object exhibits apparently purposeful
`
`motion. Examples of a salient motion include: moving from one place to another; and moving to
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`interact with another object.
`
`[88] A feature of a salient motion refers to a property of a salient motion. Examples of
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`15
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`a feature of a salient motion include: a trajectory; a length of a trajectory in image space; an
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`approximate length of a trajectory in a three-dimensional representation of the environment; a
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`position of an object in image space as a function of time; an approximate position of an object
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`in a three-dimensional representation of the environment as a function of time; a duration of a
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`trajectory; a velocity (e.g., speed and direction) in image space; an approximate velocity (e.g.,
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`20
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`speed and direction) in a three-dimensional representation of the environment; a duration of time
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`at a velocity; a change of velocity in image space; an approximate change of velocity in a three-
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`dimensional representation of the environment; a duration of a change of velocity; cessation of
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`motion; and a duration of cessation of motion. A velocity refers to the speed and direction of an
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`object at a particular time. A trajectory refers a set of (position, velocity) pairs for an object for
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`as long as the object can be tracked or for a time period.
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`[89] A scene change refers to any region of a scene that can be detected as changing
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`over a period of time. Examples of a scene change include: an stationary object leaving a scene;
`
`5
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`an object entering a scene and becoming stationary; an object changing position in a scene; and
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`an object changing appearance (e.g. color, shape, or size).
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`[90] A feature of a scene change refers to a property of a scene change. Examples of a
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`feature of a scene change include: a size of a scene change in image space; an approximate size
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`of a scene change in a three-dimensional representation of the environment; a time at which a
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`scene change occurred; a location of a scene change in image space; and an approximate location
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`of a scene change in a three-dimensional representation of the environment.
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`[91] A pre..:defined model refers to an a priori known model of an object. Examples of
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`a pre-defined include: an adult; a child; a vehicle; and a semi-trailer.
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`[92]
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`In block 31, one or more objects types of interests are identified in terms of video
`
`15
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`primitives or abstractions thereof. Examples of one or more objects include: an object; a person;
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`a red object; two objects; two persons; and a vehicle.
`
`[93]
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`In block 32, one or more spatial areas of interest are identified. An area refers to
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`one or more portions of an image from a source video or a spatial portion of a scene being
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`viewed by a video sensor. An area also includes a combination of areas from various scenes
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`20
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`and!or images. An area can be an image-based space (e.g., a line, a rectangle, a polygon, or a
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`circle in a video image) or a three-dimensional space (e.g., a cube, or an area of floor space in a
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`building).
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`[94]
`
`Figure 12 illustrates identifying areas along an aisle in a grocery store. Four areas
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`are identified: coffee; soda promotion; chips snacks; and bottled water. The areas are identified
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`via a point-and-click interface with the system.
`
`[95]
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`In block 33, one or more temporal attributes of interest are optionally identified.
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`5
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`Examples of a temporal attribute include: every 15 minutes; between 9:00p.m. to 6:30 a.m.; less
`
`than 5 minutes; longer than 30 seconds; over the weekend; and within 20 minutes of.
`
`[96]
`
`In block 34, a response is optionally identified. Examples of a response includes
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`the following: activating a visual and/or audio alert on a system display; activating a visual
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`and/or audio alarm system at the location; activating a silent alarm; activating a rapid response
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`~~:Fi:
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`~:to mechanism; locking a door; contacting a security service; forwarding data (e.g., image data,
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`video data, video primitives; and/or analyzed data) to another computer system via a network,
`
`such as the Internet; saving such data to a designated computer-readable medium; activating
`
`some other sensor or surveillance system; tasking the computer system 11 and/or another
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`computer system; and directing the computer system 11 and/or another computer system.
`
`[97]
`
`In block 35, one or more discriminators are identified by describing interactions
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`between video primitives (or their abstractions), spatial areas of interest, and temporal attributes
`
`of interest. An interaction is determined for a combination of one or more objects identified in
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`block 31, one or more spatial areas of interest identified in block 32, and one or more temporal
`
`attributes of interest identified in block 33. One or more responses identified in block 34 are
`
`20
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`optionally associated with each event discriminator.
`
`[98] Examples of an event discriminator for a single object include: an object appears;
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`a person appears; and a red object moves faster than 1 Ornls.
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`[99] Examples of an event discriminator for multiple objects include: two objects
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`come together; a person exits a vehicle; and a red object moves next to a blue object.
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`[1 00] Examples of an event discriminator for an object and a spatial attribute include:
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`an object crosses a line; an object enters an area; and a person crosses a line from the left.
`
`5
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`[1 01] Examples of an event discriminator for an object and a temporal attribute include:
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`an object appears at 10:00 p.m.; a person travels faster then 2m/s between 9:00 a.m. and 5:00
`
`p.m.; and a vehicle appears on the weekend.
`
`[1 02] Examples of an event discriminator for an object, a spatial attribute, and a
`
`temporal attribute include: a person crosses a line between midnight and 6:00a.m.; and a vehicle
`
`~p stops in an area for longer than 1 0 minutes.
`
`[103] An example of an event discriminator for an object, a spatial attribute, and a
`
`temporal attribute associated with a response include: a person enters an area between midnight
`
`and 6:00 a.m., and a security service is notified.
`
`[104]
`
`In block 24 of Figure 2, the video surveillance system is operated. The video
`
`15
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`surveillance system of the invention operates automatically, detects and archives video
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`primitives of objects in the scene, and detects event occurrences in real time using event
`
`discriminators. In addition, action is taken in real time, as appropriate, such as activating alarms,
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`generating reports, and generating output. The reports and output can be displayed and/or stored
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`locally to the system or elsewhere via a network, such as the Internet. Figure 4 illustrates a flow
`
`20
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`diagram for operating the video surveillance system.
`
`[105] In block 41, the computer system 11 obtains source video from the video sensors
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`14 and! or the video recorders 15.
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`[1 06]
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`In block 42, video primitives are extracted in real time from the source video. As
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`an option, non-video primitives can be obtained and/or extracted from one or more other sensors
`
`17 and used with the invention. The extraction of video primitives is illustrated with Figure 5.
`
`[1 07] Figure 5 illustrates a flow diagram for extracting video primitives for the video
`
`5
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`surveillance system. Blocks 51 and 52 operate in parallel and can be performed in any order or
`
`concurrently. In block 51, objects are detected via movement. Any motion detection algorithm
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`for detecting movement between frames at the pixel level can be used for this block. As an
`
`example, the three frame differencing technique can be used, which is discussed in { 1 } . The
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`detected objects are forwarded to block 53.
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`:~t:o
`
`[108]
`
`In block 52, objects are detected via change. Any change detection algorithm for
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`detecting changes from a background model can be used for this block. An object is detected in
`
`'"
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`this block if one or more pixels in a frame are deemed to be in the foreground of the frame
`
`because the pixels do not conform to a background model of the frame. As an example, a
`
`stochastic background modeling technique, such as dynamically adaptive background
`
`1~1'5
`
`subtraction, can be used, which is