Scene understanding by statistical modeling of motion patterns

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2010, Pages 2069-2076

  Saleemi, Imran ^a   Hartung, Lance ^b   Shah, Mubarak ^a  

^a UNIVERSITY OF CENTRAL FLORIDA   (United States)

^b University of Wisconsin Madison   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX SCENES; CONDITIONAL EXPECTATION; DENSE OPTICAL FLOW; GAUSSIAN MIXTURE MODEL; K-MEANS; KL-DIVERGENCE; LOW-LEVEL CUES; MIXTURE DISTRIBUTIONS; MIXTURE MODEL; MOTION MODELS; MOTION PATTERN; NOVEL METHODS; OBJECT DETECTION; PEDESTRIAN AND VEHICULAR; PIXEL LEVEL; SCENE UNDERSTANDING; SPACE AND TIME; STATIC CAMERAS; STATISTICAL MODELING; STATISTICAL REPRESENTATIONS;

COMPUTER VISION; IMAGE SEGMENTATION; MIXTURES; OBJECT RECOGNITION; TIME AND MOTION STUDY;

OPTICAL FLOWS;

EID: 77956006563     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539884     Document Type: Conference Paper

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