WO2012092148A3 - Scene activity analysis using statistical and semantic feature learnt from object trajectory data - Google Patents

Scene activity analysis using statistical and semantic feature learnt from object trajectory data Download PDF

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Publication number
WO2012092148A3
WO2012092148A3 PCT/US2011/066962 US2011066962W WO2012092148A3 WO 2012092148 A3 WO2012092148 A3 WO 2012092148A3 US 2011066962 W US2011066962 W US 2011066962W WO 2012092148 A3 WO2012092148 A3 WO 2012092148A3
Authority
WO
WIPO (PCT)
Prior art keywords
trajectory
abnormal
scene
activity analysis
statistical
Prior art date
Application number
PCT/US2011/066962
Other languages
French (fr)
Other versions
WO2012092148A2 (en
Inventor
Greg Millar
Farzin Aghdasi
Hongwei Zhu
Original Assignee
Pelco Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Pelco Inc. filed Critical Pelco Inc.
Priority to CN201180068372.2A priority Critical patent/CN103392187B/en
Priority to EP11853102.9A priority patent/EP2659456B1/en
Priority to AU2011352412A priority patent/AU2011352412B2/en
Publication of WO2012092148A2 publication Critical patent/WO2012092148A2/en
Publication of WO2012092148A3 publication Critical patent/WO2012092148A3/en

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats

Abstract

Trajectory information of objects appearing in a scene can be used to cluster trajectories into groups of trajectories according to each trajectory's relative distance between each other for scene activity analysis. By doing so, a database of trajectory data can be maintained that includes the trajectories to be clustered into trajectory groups. This database can be used to train a clustering system, and with extracted statistical features of resultant trajectory groups a new trajectory can be analyzed to determine whether the new trajectory is normal or abnormal. Embodiments described herein, can be used to determine whether a video scene is normal or abnormal. In the event that the new trajectory is identified as normal the new trajectory can be annotated with the extracted semantic data. In the event that the new trajectory is determined to be abnormal a user can be notified that an abnormal behavior has occurred.
PCT/US2011/066962 2010-12-30 2011-12-22 Scene activity analysis using statistical and semantic feature learnt from object trajectory data WO2012092148A2 (en)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CN201180068372.2A CN103392187B (en) 2010-12-30 2011-12-22 Utilize from the statistics of object trajectory data acquisition and the scene activity analysis of semantic feature
EP11853102.9A EP2659456B1 (en) 2010-12-30 2011-12-22 Scene activity analysis using statistical and semantic feature learnt from object trajectory data
AU2011352412A AU2011352412B2 (en) 2010-12-30 2011-12-22 Scene activity analysis using statistical and semantic feature learnt from object trajectory data

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US12/981,952 2010-12-30
US12/981,952 US8855361B2 (en) 2010-12-30 2010-12-30 Scene activity analysis using statistical and semantic features learnt from object trajectory data

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WO2012092148A2 WO2012092148A2 (en) 2012-07-05
WO2012092148A3 true WO2012092148A3 (en) 2013-01-31

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PCT/US2011/066962 WO2012092148A2 (en) 2010-12-30 2011-12-22 Scene activity analysis using statistical and semantic feature learnt from object trajectory data

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US (1) US8855361B2 (en)
EP (1) EP2659456B1 (en)
CN (1) CN103392187B (en)
AU (1) AU2011352412B2 (en)
WO (1) WO2012092148A2 (en)

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Publication number Publication date
US20120170802A1 (en) 2012-07-05
EP2659456A2 (en) 2013-11-06
AU2011352412B2 (en) 2016-07-07
EP2659456B1 (en) 2020-02-19
AU2011352412A1 (en) 2013-07-11
WO2012092148A2 (en) 2012-07-05
EP2659456A4 (en) 2017-03-22
CN103392187B (en) 2016-02-03
CN103392187A (en) 2013-11-13
US8855361B2 (en) 2014-10-07

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