A general Bayesian algorithm for visual object tracking based on sparse features

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2011, Pages 1181-1184

  Soto, Mauricio ^a   Regazzoni, Carlo S ^a  

^a UNIVERSITY OF GENOA   (Italy)

Author keywords

Generalized Hough Transform; Nonretinotopic Representation; Probabilistic Graphical Models; Visual Object Tracking

Indexed keywords

BAYESIAN ALGORITHMS; GENERALIZED HOUGH TRANSFORM; HUMAN VISUAL CORTEX; INTERNAL STRUCTURE; MISSED DETECTIONS; MOVING OBJECTS; NONRETINOTOPIC REPRESENTATION; OBSERVATION MODEL; PROBABILISTIC GRAPHICAL MODELS; RECURSIVE ALGORITHMS; SPARSE IMAGES; TIME SLICE; VISUAL OBJECT TRACKING;

CONTENT BASED RETRIEVAL; GRAPHIC METHODS; HOUGH TRANSFORMS; MESSAGE PASSING; SIGNAL PROCESSING; SPEECH COMMUNICATION; SPEECH RECOGNITION; TRACKING (POSITION);

ALGORITHMS;

EID: 80051657476     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2011.5946620     Document Type: Conference Paper

References (4)

1. Alessio Dore, Mauricio Soto, and Carlo S Regazzoni, "An Overview on Bayesian Tracking for Video Analytics," IEEE Signal Processing Magazine, 2010.
2. H. Ogmen and M.H. Herzog, "The geometry of visual perception: Retinotopic and nonretinotopic representations in the human visual system," Proceedings of the IEEE, vol. 98, no. 3, pp. 479 -492, march 2010.
3. D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
4. D. H. Ballard, "Generalizing the hough transform to detect arbitrary shapes," Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981.