A Gaussian process guided particle filter for tracking 3d human pose in video

IEEE Transactions on Image Processing

Volumn 22, Issue 11, 2013, Pages 4286-4300

  Sedai, Suman ^a,b   Bennamoun, Mohammed ^a   Huynh, Du Q ^a  

^a UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

^b IBM RESEARCH   (United States)

Author keywords

3D human pose tracking; Gaussian process regression; hybrid method; particle filter

Indexed keywords

ANNEALED PARTICLE FILTERS; GAUSSIAN PROCESS REGRESSION; HUMAN POSE TRACKING; HYBRID METHOD; MIXTURE OF GAUSSIANS; PARTICLE FILTER; STATE-OF-THE-ART APPROACH; SUPERVISED LEARNING METHODS;

ANNEALING; GAUSSIAN NOISE (ELECTRONIC); MONTE CARLO METHODS; THREE DIMENSIONAL; VIDEO RECORDING;

GAUSSIAN DISTRIBUTION;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BIOMETRY; BODY POSTURE; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; HUMAN; IMAGE ENHANCEMENT; NORMAL DISTRIBUTION; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; ARTICLE; METHODOLOGY;

ALGORITHMS; BIOMETRY; COMPUTER SIMULATION; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; POSTURE; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84884569763     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2013.2271850     Document Type: Article

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