Tracking human motion using auxiliary particle filters and iterated likelihood weighting

This source preferred by Hammadi Nait-Charif

Authors: McKenna, S.J. and Nait-Charif, H.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V09-4KGX84W-2&_user=1682380&_coverDate=06%2F01%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000011378&_version=1&_urlVersion=0&_userid=1682380&md5=166b6f33e89ac4d2848b66b09c707192

Journal: Image and Vision Computing

Volume: 25

Pages: 852-862

ISSN: 0262-8856

DOI: 10.1016/j.imavis.2006.06.003

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.

This data was imported from DBLP:

Authors: McKenna, S.J. and Nait-Charif, H.

Journal: Image Vis. Comput.

Volume: 25

Pages: 852-862

This data was imported from Scopus:

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

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.

This data was imported from Web of Science (Lite):

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

The data on this page was last updated at 05:24 on October 27, 2020.