Moving object detection by detecting contiguous outliers in the low-rank representation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 35, Issue 3, 2013, Pages 597-610

  Zhou, Xiaowei ^a   Yang, Can ^a   Yu, Weichuan ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

low rank modeling; Markov Random Fields; motion segmentation; Moving object detection

Indexed keywords

AUTOMATED VIDEO; BACKGROUND MODEL; BACKGROUND SUBTRACTION; BACKGROUND SUBTRACTION TECHNIQUES; BINARY CLASSIFIERS; CRITICAL TASKS; DYNAMIC BACKGROUND; LOW RANK; LOW-RANK REPRESENTATIONS; MARKOV RANDOM FIELDS; MOTION INFORMATION; MOTION SEGMENTATION; MOTION-BASED METHODS; MOVING-OBJECT DETECTION; NON-RIGID MOTION; OBJECT DETECTION; OBJECT DETECTORS; SINGLE PROCESS; STATE-OF-THE-ART APPROACH; TRAINING PHASE; TRAINING SEQUENCES; UNIFIED FRAMEWORK; VISION APPLICATIONS;

MARKOV PROCESSES; MOTION ESTIMATION; STATISTICS;

OBJECT RECOGNITION;

EID: 84874545895     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2012.132     Document Type: Article

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