Conditional regression forests for human pose estimation

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

Volumn , Issue , 2012, Pages 3394-3401

  Sun, Min ^a,b   Kohli, Pushmeet ^a   Shotton, Jamie ^a  

^a MICROSOFT RESEARCH   (United Kingdom)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BODY PARTS; COMPUTATIONAL COSTS; DEPENDENCY RELATIONSHIP; DEPTH IMAGE; FOREST MODELS; HIGH-LEVEL COMPUTER VISION; HUMAN HEIGHT; HUMAN POSE ESTIMATIONS; HUMAN SUBJECTS; LATENT VARIABLE; LOCATION PREDICTION; OBJECT SEGMENTATION; OUTPUT VARIABLES; POSE ESTIMATION; PRIOR KNOWLEDGE; PROBLEM INSTANCES; RANDOM FORESTS; REGRESSION MODEL; STATE-OF-THE-ART METHODS; TEMPORAL MODELS;

DECISION TREES; REGRESSION ANALYSIS;

COMPUTER VISION;

EID: 84866654638     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6248079     Document Type: Conference Paper

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