Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts

Neurocomputing

Volumn 143, Issue , 2014, Pages 144-152

  Xu, Dan ^a,b   Song, Rui ^c   Wu, Xinyu ^a,b   Li, Nannan ^a   Feng, Wei ^a   Qian, Huihuan ^b  

^a SHENZHEN INSTITUTES OF ADVANCED TECHNOLOGY   (China)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^c SHANDONG UNIVERSITY   (China)

Author keywords

Energy function; Hierarchical discovery; Video anomaly detection; Visual surveillance

Indexed keywords

COMPUTER APPLICATIONS; NEURAL NETWORKS;

ACTIVITY DISCOVERIES; ACTIVITY PATTERNS; ANOMALY DETECTION; DISCOVERY FRAMEWORKS; ENERGY FUNCTIONS; HIERARCHICAL DISCOVERY; UNSUPERVISED METHOD; VISUAL SURVEILLANCE;

SECURITY SYSTEMS;

ARTICLE; AUTOMATED PATTERN RECOGNITION; COMPUTER ANALYSIS; COMPUTER MODEL; CONTROLLED STUDY; DATA MINING; HIERARCHICAL ACTIVITY DISCOVERY; IMAGE ANALYSIS; IMAGE DISPLAY; IMAGE PROCESSING; IMAGING SYSTEM; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; SYSTEM ANALYSIS; VIDEO ANOMALY DETECTION; VIDEORECORDING; VISUAL INFORMATION;

EID: 84904812644     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.06.011     Document Type: Article

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