Tracking human motion using auxiliary particle filters and iterated likelihood weighting
Authors: McKenna, S.J. and Nait-Charif, H.
Journal: Image and Vision Computing
Volume: 25
Issue: 6
Pages: 852-862
ISSN: 0262-8856
DOI: 10.1016/j.imavis.2006.06.003
Abstract:Bayesian particle filters have become popular for tracking human motion in cluttered scenes. The most commonly used filters suffer from two drawbacks. First, the prior used for the filtering step is often poor due to relatively large, poorly modelled inter-frame motion. Second, the use of the prior as an importance function results in inefficient sampling of the posterior. The use of the auxiliary particle filter (APF) and the novel iterated likelihood weighting filter (ILW) are proposed here in order to help address these problems. Experimental results comparing the filters' accuracy and consistency are presented for a scenario in which a person is tracked in an overhead view using an ellipse model. A likelihood model based on combined region (colour) and boundary (gradient) cues is motivated and used. The ILW filter is shown to outperform both Condensation and the APF on typical sequences from this scenario. © 2006 Elsevier B.V. All rights reserved.
Source: Scopus
Tracking human motion using auxiliary particle filters and iterated likelihood weighting
Authors: McKenna, S.J. and Nait-Charif, H.
Journal: IMAGE AND VISION COMPUTING
Volume: 25
Issue: 6
Pages: 852-862
eISSN: 1872-8138
ISSN: 0262-8856
DOI: 10.1016/j.imavis.2006.06.003
Source: Web of Science (Lite)
Tracking human motion using auxiliary particle filters and iterated likelihood weighting
Authors: McKenna, S.J. and Nait-Charif, H.
Journal: Image and Vision Computing
Volume: 25
Pages: 852-862
ISSN: 0262-8856
DOI: 10.1016/j.imavis.2006.06.003
Abstract:Bayesian particle filters have become popular for tracking human motion in cluttered scenes. The most commonly used filters suffer from two drawbacks. First, the prior used for the filtering step is often poor due to relatively large, poorly modelled inter-frame motion. Second, the use of the prior as an importance function results in inefficient sampling of the posterior. The use of the auxiliary particle filter (APF) and the novel iterated likelihood weighting filter (ILW) are proposed here in order to help address these problems. Experimental results comparing the filters’ accuracy and consistency are presented for a scenario in which a person is tracked in an overhead view using an ellipse model. A likelihood model based on combined region (colour) and boundary (gradient) cues is motivated and used. The ILW filter is shown to outperform both Condensation and the APF on typical sequences from this scenario.
Source: Manual
Preferred by: Hammadi Nait-Charif
Tracking human motion using auxiliary particle filters and iterated likelihood weighting.
Authors: McKenna, S.J. and Nait-Charif, H.
Journal: Image Vis. Comput.
Volume: 25
Pages: 852-862
DOI: 10.1016/j.imavis.2006.06.003
Source: DBLP