`
`US007868912B2
`
`US 7,868,912 B2
`c12) United States Patent
`(IO) Patent No.:
`(45)Date of Patent:
`Jan. 11, 2011
`
`Venetianer et al.
`
`(54)VIDEO SURVEILLANCE SYSTEM
`
`
`EMPLOYING VIDEO PRIMITIVES
`
`(58)Field of Classification Search ................. 348/143,
`
`
`
`348/148, 150,149,166,169,170; 382/103,
`
`
`382/115; 375/240.02, 240.08;H04N 7/18
`
`
`
`See application file for complete search history.
`
`References Cited
`
`
`
`U.S. PATENT DOCUMENTS
`
`(75)Inventors: Peter L. Venetianer, McLean, VA (US);
`
`
`
`
`
`
`Alan J. Lipton, Herndon, VA (US);
`(56)
`
`
`Andrew J. Chosak, Arlington, VA (US);
`
`
`Matthew F. Frazier, Arlington, VA
`
`
`(US); Niels Haering, Reston, VA (US);
`
`
`GaryW. Myers, Ashburn, VA (US);
`
`
`Weihong Yin, Herndon, VA (US);
`
`Zhong Zhang, Herndon, VA (US)
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`(73)Assignee: ObjectVideo, Inc., Reston, VA (US)
`
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`
`(Continued)
`
`(21)Appl. No.: 11/098,385
`
`
`
`FOREIGN PATENT DOCUMENTS
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`
`
`(22)Filed:Apr. 5, 2005
`
`EP
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`
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`
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`
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`
`
`
`
`
`Related U.S. Application Data
`
`(Continued)
`
`
`
`Primary Examiner-Tung Vo
`
`
`
`
`(63) Continuation-in-part of application No. 11/057,154,
`
`
`
`filed on Feb. 15, 2005, which is a continuation-in-part
`PLLC
`
`
`of application No. 09/987, 707, filed on Nov. 15, 2001,
`
`
`now abandoned, which is a continuation-in-part of
`(57)
`ABSTRACT
`
`
`application No. 09/694,712, filed on Oct. 24, 2000,
`A video surveillance system extracts video primitives and
`
`
`now Pat. No. 6,954,498.
`
`
`
`
`extracts event occurrences from the video primitives using
`
`
`event discriminators. The system can undertake a response,
`
`
`such as an alarm, based on extracted event occurrences.
`
`(51) Int. Cl.
`
`H04N 7118 (2006.01)
`
`(74) Attorney, Agent, or Firm-Muir Patent Consulting,
`
`
`
`
`22 Claims, 19 Drawing Sheets
`
`(52)U.S. Cl. ...................................................... 348/143
`
`14
`
`video
`sensors
`
`15
`
`video
`recorders
`
`other
`sensors
`
`17
`
`11
`
`
`
`computer system
`
`computer
`
`computer-readable
`medium
`
`12
`
`13
`
`16
`
`1/0 devices
`
`
`
`US 7,868,912 B2
`Page 2
`
`U.S. PATENT DOCUMENTS
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`
`* cited by examiner
`
`
`
`system
`
`surveillance
`
`video
`operate
`
`"
`
`24
`
`..
`
`-
`
`system
`
`surveillance
`
`video
`task
`
`"
`
`23
`
`FIG. 2
`
`..
`
`-
`
`system
`
`surveillance
`
`video
`
`calibrate
`
`"
`
`22
`
`.._
`
`..
`
`1/0 devices
`
`"
`
`16
`
`~
`
`-
`
`~
`
`...
`
`FIG. 1
`
`13
`
`\
`
`'-
`l
`
`I
`
`12
`(
`
`medium
`
`computer-readable
`
`computer
`
`computer system
`
`"
`
`11
`
`~
`
`.._
`
`~
`
`-
`
`~
`
`-
`
`-
`
`...
`
`~
`
`...
`
`~
`
`...
`
`system
`
`surveillance
`
`video
`set up
`
`\
`
`21
`
`"'
`
`17
`
`sensors
`other
`
`recorders
`
`video
`
`15 '-
`
`sensors
`video
`
`14 "
`
`
`
`as appropriate
`
`response,
`undertake
`
`...
`
`-
`
`"
`
`94
`
`occurrences
`
`event
`extract
`
`'
`
`93
`
`FIG. 9
`
`...
`
`-
`
`primitives
`
`...
`~ archived video
`
`access
`
`"
`
`92
`
`system
`
`surveillance
`
`video
`task
`
`"
`
`91
`
`.... -c
`0 -.
`N
`.....
`=-
`
`rJJ
`
`('D
`('D
`
`....
`0 ....
`....
`? ....
`""' ~
`
`N
`
`~
`
`=
`
`~
`~
`~
`~
`•
`00
`~
`
`~
`
`as appropriate
`
`occurrences
`
`response,
`undertake
`
`event
`extract
`
`FIG.4
`
`primitives
`
`video
`archive
`
`primitives
`
`video
`extract
`
`video
`source
`obtain
`
`45
`
`44
`
`43
`
`42
`
`interactions
`
`identify
`
`responses
`
`identify
`
`FIG. 3
`
`attributes
`temporal
`identify
`
`areas
`spatial
`identify
`
`objects
`identify
`
`35
`
`34
`
`33
`
`32
`
`41
`
`31
`
`
`
`FIG.6
`
`output
`
`generate
`
`activity record
`
`generate
`
`as appropriate
`
`response,
`undertake
`
`63
`
`62
`
`61
`
`FIG. 5
`
`52
`
`via change
`
`L--~ detect objects .,__ ___ ___.
`
`primitives
`
`video
`identify
`
`objects
`classify
`
`57
`
`56
`
`is salient
`
`object
`
`foreground
`trajectory of
`determine if
`
`55
`
`objects
`track
`
`blobs
`
`generate
`
`via motion
`
`___ _.,. detect objects
`
`54
`
`53
`
`51
`
`
`
`typical object
`
`monitor
`
`FIG. 8
`
`areas
`
`trackable
`identify
`
`86
`
`87
`
`83
`
`via change
`
`detect objects _____ _
`
`typical objects
`typical sizes of
`
`identify
`
`88
`
`objects
`track
`
`blobs
`
`generate
`
`detect objects 1-----1"
`
`t----.r--...i via motion
`
`source video
`
`obtain
`
`85
`
`84
`
`FIG. 7
`
`82
`
`73
`
`detect objects ._ ____ .......
`
`via change
`
`81
`
`typical object
`
`sizes of
`
`identify typical
`
`77
`
`typical object
`
`monitor
`
`objects
`track
`
`blobs
`
`generate
`
`detect objects 1---.....
`
`.----..--..... via motion
`
`source video
`
`obtain
`
`76
`
`75
`
`74
`
`72
`
`71
`
`
`
`U.S. Patent
`U.S. Patent
`
`Jan. 11,2011
`Jan. 11, 2011
`
`Sheet 5 of 19
`Sheet 5 of 19
`
`US 7,868,912 B2
`US 7,868,912 B2
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`US 7,868,912 B2
`
`1
`VIDEO SURVEILLANCE SYSTEM
`EMPLOYING VIDEO PRIMITIVES
`
`CROSS-REFERENCE TO RELATED
`APPLICATIONS
`
`This application is a continuation-in-part of U.S. patent
`application Ser. No.11/057,154, filedonFeb.15, 2005, which
`is a continuation-in-part of U.S. patent application Ser. No.
`09/987,707, filed on Nov. 15, 2001, which claims the priority
`of U.S. patent application Ser. No. 09/694,712, filed on Oct.
`24, 2000, all of which are incorporated herein by reference.
`
`BACKGROUND OF THE INVENTION
`
`Field of the Invention
`
`The invention relates to a system for automatic video sur(cid:173)
`veillance employing video primitives.
`
`REFERENCES
`
`2
`The following references describe blob analysis for trucks,
`cars, and people:
`{14} Collins, Lipton, Kanade, Fujiyoshi, Duggins, Tsin,
`Tolliver, Enomoto, and Hasegawa, "A System for Video Sur-
`5 veillance and Monitoring: VSAM Final Report," Technical
`Report CMU-RI-TR-00-12, Robotics Institute, Carnegie
`Mellon University, May 2000.
`{15} Lipton, Fujiyoshi, and Patil, "Moving Target Classi(cid:173)
`fication and Tracking from Real-time Video," 98 Darpa IUW,
`10 November 20-23, 1998.
`The following reference describes analyzing a single-per(cid:173)
`son blob and its contours:
`{16} C. R. Wren, A. Azarbayejani, T. Darrell, and A. P.
`Pentland. "Pfinder: Real-Time Tracking of the Human Body,"
`15 PAM!, vol 19, pp. 780-784, 1997.
`The following reference describes internal motion of
`blobs, including any motion-based segmentation:
`{ 17} M. Allmen and C. Dyer, "Long-Range Spatiotem-
`20 poral Motion Understanding Using Spatiotemporal Flow
`Curves," Proc. IEEE CVPR, Lahaina, Maui, Hawaii, pp. 303-
`309, 1991.
`{18} L. Wixson, "Detecting Salient Motion by Accumu(cid:173)
`lating Directionally Consistent Flow", IEEE Trans. Pattern
`25 Anal. Mach. Intel!., vol. 22, pp. 774-781, August 2000.
`
`BACKGROUND OF THE INVENTION
`
`Video surveillance of public spaces has become extremely
`widespread and accepted by the general public. Unfortu(cid:173)
`nately, conventional video surveillance systems produce such
`prodigious volumes of data that an intractable problem results
`in the analysis of video surveillance data.
`A need exists to reduce the amount of video surveillance
`35 data so analysis of the video surveillance data can be con(cid:173)
`ducted.
`A need exists to filter video surveillance data to identify
`desired portions of the video surveillance data.
`
`SUMMARY OF THE INVENTION
`
`For the convenience of the reader, the references referred to
`herein are listed below. In the specification, the numerals
`within brackets refer to respective references. The listed ref(cid:173)
`erences are incorporated herein by reference.
`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," Pro(cid:173)
`ceedings of IEEE WACV '98, Princeton, N.J., 1998, pp. 8-14. 30
`{ 2} W. E. L. Grimson, et al., "Using Adaptive Tracking to
`Classify and Monitor Activities in a Site", CVPR, pp. 22-29,
`June 1998.
`{3} A. J. Lipton, H. Fujiyoshi, R. S. Patil, "Moving Target
`Classification and Tracking from Real-time Video," IUW, pp.
`129-136, 1998.
`{ 4} T. J. Olson and F. Z. Brill, "Moving Object Detection
`and Event Recognition Algorithm for Smart Cameras," IUW,
`pp. 159-175, May 1997.
`The following references describe detecting and tracking 40
`humans:
`{ 5} A. J. Lipton, "Local Application of Optical Flow to
`Analyse Rigid Versus Non-Rigid Motion," International
`Conference on Computer Vision, Corfu, Greece, September
`1999.
`{ 6} F. Bartolini, V. Cappellini, and A. Mecocci, "Counting
`people getting in and out of a bus by real-time image-se(cid:173)
`quence processing," IVC, 12(1):36-41, January 1994.
`{7} M. Rossi and A. Bozzoli, "Tracking and counting
`moving people," ICIP94, pp. 212-216, 1994.
`{8} C.R. Wren, A. Azarbayejani, T. Darrell, and A. Pent(cid:173)
`land, "Pfinder: Real-time tracking of the human body," Vis(cid:173)
`mod, 1995.
`{9} L. Khoudour, L. Duvieubourg, J. P. Deparis, "Real(cid:173)
`Time Pedestrian Counting by Active Linear Cameras," JEI,
`5(4):452-459, October 1996.
`{10} S. Ioffe, D. A. Forsyth, "Probabilistic Methods for
`Finding People," IJCV 43(1 ):45-68, June 2001.
`{ 11} M. Isard and J. MacCormick, "BraMBLe: A Baye- 60
`sian Multiple-Blob Tracker," !CCV, 2001.
`The following references describe blob analysis:
`{12} D. M. Gavrila, "The Visual Analysis of Human
`Movement: A Survey," CVIU, 73(1):82-98, January 1999.
`{13} Niels Haering and Niels da Vitoria Lobo, "Visual 65
`Event Detection," Video Computing Series, Editor Mubarak
`Shah, 2001.
`
`45
`
`50
`
`An object of the invention is to reduce the amount of video
`surveillance data so analysis of the video surveillance data
`can be conducted.
`An object of the invention is to filter video surveillance data
`to identify desired portions of the video surveillance data.
`An object of the invention is to produce a real time alarm
`based on an automatic detection of an event from video sur(cid:173)
`veillance data.
`An object of the invention is to integrate data from surveil(cid:173)
`lance sensors other than video for improved searching capa(cid:173)
`bilities.
`An object of the invention is to integrate data from surveil-
`55 lance sensors other than video for improved event detection
`capabilities
`The invention includes an article of manufacture, a
`method, a system, and an apparatus for video surveillance.
`The article of manufacture of the invention includes a
`computer-readable medium comprising software for a video
`surveillance system, comprising code segments for operating
`the video surveillance system based on video primitives.
`The article of manufacture of the invention includes a
`computer-readable medium comprising software for a video
`surveillance system, comprising code segments for accessing
`archived video primitives, and code segments for extracting
`event occurrences from accessed archived video primitives.
`
`
`
`US 7,868,912 B2
`
`3
`The system of the invention includes a computer system
`including a computer-readable medium having software to
`operate a computer in accordance with the invention.
`The apparatus of the invention includes a computer includ(cid:173)
`ing a computer-readable medium having software to operate
`the computer in accordance with the invention.
`The article of manufacture of the invention includes a
`computer-readable medium having software to operate a
`computer in accordance with the invention.
`Moreover, the above objects and advantages of the inven- 10
`tion are illustrative, and not exhaustive, of those that can be
`achieved by the invention. Thus, these and other objects and
`advantages of the invention will be apparent from the descrip(cid:173)
`tion herein, both as embodied herein and as modified in view
`of any variations which will be apparent to those skilled in the
`art.
`
`4
`a carrier wave used to carry computer-readable electronic
`data, such as those used in transmitting and receiving e-mail
`or in accessing a network.
`"Software" refers to prescribed rules to operate a computer.
`5 Examples of software include: software; code segments;
`instructions; computer programs; and progranimed logic.
`A "computer system" refers to a system having a computer,
`where the computer comprises a computer-readable medium
`embodying software to operate the computer.
`A "network" refers to a number of computers and associ-
`ated devices that are connected by communication facilities.
`A network involves permanent connections such as cables or
`temporary connections such as those made through telephone
`or other communication links. Examples of a network
`15 include: an internet, such as the Internet; an intranet; a local
`area network (LAN); a wide area network (WAN); and a
`combination of networks, such as an internet and an intranet.
`
`DEFINITIONS
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`25
`
`30
`
`A "video" refers to motion pictures represented in analog 20
`and/or digital form. Examples of video include: television,
`movies, image sequences from a video camera or other
`observer, and computer-generated image sequences.
`A "frame" refers to a particular image or other discrete unit
`within a video.
`An "object" refers to an item of interest in a video.
`Examples of an object include: a person, a vehicle, an animal,
`and a physical subject.
`An "activity" refers to one or more actions and/or one or
`more composites of actions of one or more objects. Examples
`of an activity include: entering; exiting; stopping; moving;
`raising; lowering; growing; and shrinking.
`A "location" refers to a space where an activity may occur.
`A location can be, for example, scene-based or image-based.
`Examples of a scene-based location include: a public space; a
`store; a retail space; an office; a warehouse; a hotel room; a
`hotel lobby; a lobby of a building; a casino; a bus station; a
`train station; an airport; a port; a bus; a train; an airplane; and
`a ship. Examples of an image-based location include: a video 40
`image; a line in a video image; an area in a video image; a
`rectangular section of a video image; and a polygonal section
`of a video image.
`An "event" refers to one or more objects engaged in an
`activity. The event may be referenced with respect to a loca(cid:173)
`tion and/or a time.
`A "computer" refers to any apparatus that is capable of
`accepting a structured input, processing the structured input
`according to prescribed rules, and producing results of the
`processing as output. Examples of a computer include: a 50
`computer; a general purpose computer; a supercomputer; a
`mainframe; a super mini-computer; a mini-computer; a work(cid:173)
`station; a micro-computer; a server; an interactive television;
`a hybrid combination of a computer and an interactive tele(cid:173)
`vision; and application-specific hardware to emulate a com(cid:173)
`puter and/or software. A computer can have a single processor
`or multiple processors, which can operate in parallel and/or
`not in parallel. A computer also refers to two or more com(cid:173)
`puters connected together via a network for transmitting or
`receiving information between the computers. An example of
`such a computer includes a distributed computer system for
`processing information via computers linked by a network.
`A "computer-readable medium" refers to any storage
`device used for storing data accessible by a computer.
`Examples of a computer-readable medium include: a mag- 65
`netic hard disk; a floppy disk; an optical disk, such as a
`CD-ROM and a DVD; a magnetic tape; a memory chip; and
`
`Embodiments of the invention are explained in greater
`detail by way of the drawings, where the same reference
`numerals refer to the same features.
`FIG. 1 illustrates a plan view of the video surveillance
`system of the invention.
`FIG. 2 illustrates a flow diagram for the video surveillance
`system of the invention.
`FIG. 3 illustrates a flow diagram for tasking the video
`surveillance system.
`FIG. 4 illustrates a flow diagram for operating the video
`surveillance system.
`FIG. 5 illustrates a flow diagram for extracting video primi(cid:173)
`tives for the video surveillance system.
`FIG. 6 illustrates a flow diagram fo