WO2016069902A3 - Background foreground model with dynamic absorbtion window and incremental update for background model thresholds - Google Patents

Background foreground model with dynamic absorbtion window and incremental update for background model thresholds Download PDF

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Publication number
WO2016069902A3
WO2016069902A3 PCT/US2015/058071 US2015058071W WO2016069902A3 WO 2016069902 A3 WO2016069902 A3 WO 2016069902A3 US 2015058071 W US2015058071 W US 2015058071W WO 2016069902 A3 WO2016069902 A3 WO 2016069902A3
Authority
WO
WIPO (PCT)
Prior art keywords
background
model
foreground
background model
based approach
Prior art date
Application number
PCT/US2015/058071
Other languages
French (fr)
Other versions
WO2016069902A2 (en
WO2016069902A9 (en
Inventor
Kishor Adinath Saitwal
Lon W. Risinger
Wesley Kenneth Cobb
Original Assignee
Behavioral Recognition Systems, 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
Priority claimed from US14/526,815 external-priority patent/US9471844B2/en
Priority claimed from US14/526,879 external-priority patent/US9460522B2/en
Application filed by Behavioral Recognition Systems, Inc. filed Critical Behavioral Recognition Systems, Inc.
Publication of WO2016069902A2 publication Critical patent/WO2016069902A2/en
Publication of WO2016069902A3 publication Critical patent/WO2016069902A3/en
Publication of WO2016069902A9 publication Critical patent/WO2016069902A9/en
Priority to US15/582,558 priority Critical patent/US10373340B2/en
Priority to US16/456,470 priority patent/US10916039B2/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30236—Traffic on road, railway or crossing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)
  • Studio Circuits (AREA)

Abstract

Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds.
PCT/US2015/058071 2014-10-29 2015-10-29 Background foreground model with dynamic absorbtion window and incremental update for background model thresholds WO2016069902A2 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
US15/582,558 US10373340B2 (en) 2014-10-29 2017-04-28 Background foreground model with dynamic absorption window and incremental update for background model thresholds
US16/456,470 US10916039B2 (en) 2014-10-29 2019-06-28 Background foreground model with dynamic absorption window and incremental update for background model thresholds

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US14/526,815 US9471844B2 (en) 2014-10-29 2014-10-29 Dynamic absorption window for foreground background detector
US14/526,879 2014-10-29
US14/526,879 US9460522B2 (en) 2014-10-29 2014-10-29 Incremental update for background model thresholds
US14/526,815 2014-10-29

Related Parent Applications (2)

Application Number Title Priority Date Filing Date
US14/526,815 Continuation US9471844B2 (en) 2014-10-29 2014-10-29 Dynamic absorption window for foreground background detector
US14/526,879 Continuation US9460522B2 (en) 2014-10-29 2014-10-29 Incremental update for background model thresholds

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US15/582,558 Continuation US10373340B2 (en) 2014-10-29 2017-04-28 Background foreground model with dynamic absorption window and incremental update for background model thresholds

Publications (3)

Publication Number Publication Date
WO2016069902A2 WO2016069902A2 (en) 2016-05-06
WO2016069902A3 true WO2016069902A3 (en) 2016-06-23
WO2016069902A9 WO2016069902A9 (en) 2017-01-05

Family

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Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2015/058071 WO2016069902A2 (en) 2014-10-29 2015-10-29 Background foreground model with dynamic absorbtion window and incremental update for background model thresholds

Country Status (1)

Country Link
WO (1) WO2016069902A2 (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10223590B2 (en) 2016-08-01 2019-03-05 Qualcomm Incorporated Methods and systems of performing adaptive morphology operations in video analytics
CN107403436B (en) * 2017-06-26 2021-03-23 中山大学 Figure outline rapid detection and tracking method based on depth image
CN110033425B (en) * 2018-01-10 2023-03-28 富士通株式会社 Interference area detection device and method and electronic equipment
CN111768377B (en) * 2020-06-28 2023-08-04 北京百度网讯科技有限公司 Image color evaluation method, device, electronic equipment and storage medium
CN113807227B (en) * 2021-09-11 2023-07-25 浙江浙能嘉华发电有限公司 Safety monitoring method, device, equipment and storage medium based on image recognition

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070273765A1 (en) * 2004-06-14 2007-11-29 Agency For Science, Technology And Research Method for Detecting Desired Objects in a Highly Dynamic Environment by a Monitoring System
US20090028432A1 (en) * 2005-12-30 2009-01-29 Luca Rossato Segmentation of Video Sequences
US20110123133A1 (en) * 2008-06-30 2011-05-26 Honeywell International Inc. Gaussian mixture model based illumination normalization for global enhancement
US20120019728A1 (en) * 2010-07-26 2012-01-26 Darnell Janssen Moore Dynamic Illumination Compensation For Background Subtraction

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070273765A1 (en) * 2004-06-14 2007-11-29 Agency For Science, Technology And Research Method for Detecting Desired Objects in a Highly Dynamic Environment by a Monitoring System
US20090028432A1 (en) * 2005-12-30 2009-01-29 Luca Rossato Segmentation of Video Sequences
US20110123133A1 (en) * 2008-06-30 2011-05-26 Honeywell International Inc. Gaussian mixture model based illumination normalization for global enhancement
US20120019728A1 (en) * 2010-07-26 2012-01-26 Darnell Janssen Moore Dynamic Illumination Compensation For Background Subtraction

Also Published As

Publication number Publication date
WO2016069902A2 (en) 2016-05-06
WO2016069902A9 (en) 2017-01-05

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