A self-training approach for visual tracking and recognition of complex human activity patterns

International Journal of Computer Vision

Volumn 99, Issue 2, 2012, Pages 166-189

  Bandouch, Jan ^a   Jenkins, Odest Chadwicke ^b   Beetz, Michael ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b Brown Univerity   (United States)

Author keywords

Activity recognition; Markerless human motion capture; Probabilistic state estimation; Self trained models of human motion

Indexed keywords

ACTIVITY RECOGNITION; APPEARANCE MODELS; ENVIRONMENT MODELS; EXPERIMENTAL EVALUATION; FULL-BODY MOTIONS; HIERARCHICAL SAMPLING; HUMAN ACTIVITIES; HUMAN DYNAMICS; HUMAN MODEL; HUMAN MOTION CAPTURE; HUMAN MOTIONS; LEARNING ENVIRONMENTS; LEARNING MODELS; MANIPULATION TASK; MEDICAL RESEARCH; MOTION MODELS; POTENTIAL FIELD; PROBABILISTIC FRAMEWORK; SELF-TRAINING; VISUAL TRACKING;

BIOMECHANICS; RESEARCH;

COMPUTER VISION;

EID: 84863626501     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-012-0522-y     Document Type: Article

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