WO2020171388A3 - 모션벡터의 궤적 및 패턴을 이용한 압축영상의 이상모션 객체 식별 방법 - Google Patents

모션벡터의 궤적 및 패턴을 이용한 압축영상의 이상모션 객체 식별 방법 Download PDF

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WO2020171388A3
WO2020171388A3 PCT/KR2020/000731 KR2020000731W WO2020171388A3 WO 2020171388 A3 WO2020171388 A3 WO 2020171388A3 KR 2020000731 W KR2020000731 W KR 2020000731W WO 2020171388 A3 WO2020171388 A3 WO 2020171388A3
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compressed image
motion vector
abnormal motion
identifying
motion object
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WO2020171388A2 (ko
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박정식
배현성
정승훈
이성진
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INNODEP CO Ltd
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INNODEP CO Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H04N19/137—Motion inside a coding unit, e.g. average field, frame or block difference
    • H04N19/139—Analysis of motion vectors, e.g. their magnitude, direction, variance or reliability
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/593—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving spatial prediction techniques
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06T2207/30241—Trajectory

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Multimedia (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
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  • Molecular Biology (AREA)
  • Computing Systems (AREA)
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  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Compression Or Coding Systems Of Tv Signals (AREA)
  • Image Analysis (AREA)

Abstract

본 발명은 일반적으로 H.264 AVC 및 H.265 HEVC 등의 압축영상에서 다른 객체들과는 다른 특이한 행동을 나타내는 객체, 즉 이상모션 객체를 효과적으로 식별해내는 기술에 관한 것이다. 특히, 본 발명은 예컨대 CCTV 카메라가 생성하는 압축영상에 대해 종래기술처럼 복잡한 영상분석을 통해 객체 존재를 인식하고 이들의 행동을 관찰하여 이상모션 객체를 식별하는 것이 아니라 압축영상을 파싱하여 얻는 모션벡터를 이용하여 이동객체 영역을 추출하고 모션벡터 궤적 패턴에 기초하여 이상모션 객체를 효과적으로 식별하는 기술에 관한 것이다. 특히, 본 발명은 압축영상에 얻은 모션벡터 궤적 패턴으로 훈련 데이터세트를 구성하고 신경망을 학습시켜 이상모션 객체 식별에 활용함으로써 이상모션 객체 식별의 정확도를 개선하는 기술에 관한 것이다.
PCT/KR2020/000731 2019-02-18 2020-01-15 모션벡터의 궤적 및 패턴을 이용한 압축영상의 이상모션 객체 식별 방법 Ceased WO2020171388A2 (ko)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
KR10-2019-0018846 2019-02-18
KR1020190018846A KR102177494B1 (ko) 2019-02-18 2019-02-18 모션벡터의 궤적 및 패턴을 이용한 압축영상의 이상모션 객체 식별 방법

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WO2020171388A2 WO2020171388A2 (ko) 2020-08-27
WO2020171388A3 true WO2020171388A3 (ko) 2020-10-22

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PCT/KR2020/000731 Ceased WO2020171388A2 (ko) 2019-02-18 2020-01-15 모션벡터의 궤적 및 패턴을 이용한 압축영상의 이상모션 객체 식별 방법

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WO (1) WO2020171388A2 (ko)

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CN112288050B (zh) * 2020-12-29 2021-05-11 中电科新型智慧城市研究院有限公司 一种异常行为识别方法、识别装置、终端设备及存储介质
KR20230064898A (ko) 2021-11-04 2023-05-11 한화비전 주식회사 영상정보 검색 장치 및 방법
CN114821784B (zh) * 2022-04-26 2025-06-13 中国科学院自动化研究所 基于多层次监督的多模态数据手术行为识别方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000295600A (ja) * 1999-04-08 2000-10-20 Toshiba Corp 監視装置
JP2002262296A (ja) * 2001-02-28 2002-09-13 Mitsubishi Electric Corp 移動物体検出装置、および画像監視システム
JP2005284652A (ja) * 2004-03-29 2005-10-13 Tama Tlo Kk 動きベクトルを用いた映像監視方法及び装置
KR101808587B1 (ko) * 2017-08-03 2017-12-13 주식회사 두원전자통신 객체인식과 추적감시 및 이상상황 감지기술을 이용한 지능형 통합감시관제시스템
KR101915538B1 (ko) * 2018-05-04 2018-11-06 주식회사 다누시스 모션 벡터를 이용한 객체 감지 시스템 및 모션 벡터를 이용한 객체 감지 방법

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102061915B1 (ko) * 2018-11-26 2020-01-02 이노뎁 주식회사 압축영상에 대한 신택스 기반의 객체 분류 방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000295600A (ja) * 1999-04-08 2000-10-20 Toshiba Corp 監視装置
JP2002262296A (ja) * 2001-02-28 2002-09-13 Mitsubishi Electric Corp 移動物体検出装置、および画像監視システム
JP2005284652A (ja) * 2004-03-29 2005-10-13 Tama Tlo Kk 動きベクトルを用いた映像監視方法及び装置
KR101808587B1 (ko) * 2017-08-03 2017-12-13 주식회사 두원전자통신 객체인식과 추적감시 및 이상상황 감지기술을 이용한 지능형 통합감시관제시스템
KR101915538B1 (ko) * 2018-05-04 2018-11-06 주식회사 다누시스 모션 벡터를 이용한 객체 감지 시스템 및 모션 벡터를 이용한 객체 감지 방법

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WO2020171388A2 (ko) 2020-08-27
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