Rao-blackwellised particle filter for tracking with application in visual surveillance

Xu Xinyu, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)

Abstract

Particle filters have become popular tools for visual tracking since they do not require the modeling system to be Gaussian and linear. However, when applied to a high dimensional state-space, particle filters can be inefficient because a prohibitively large number of samples may be required in order to approximate the underlying density functions with desired accuracy. In this paper, by proposing a tracking algorithm based on Rao-Blackwellised particle filter (RBPF), we show how to exploit the analytical relationship between state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, we estimate some of the state variables as in a regular particle filter, and the distributions of the remaining variables are updated analytically using an exact filter (Kalman filter in this paper). We discuss how the proposed method can be applied to facilitate the visual tracking task in typical surveillance applications. Experiments using both simulated data and real video sequences show that the proposed method results in more accurate and more efficient tracking than a regular particle filter.

Original languageEnglish (US)
Title of host publicationProceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS
Pages17-24
Number of pages8
Volume2005
DOIs
StatePublished - 2005
Event2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS - Beijing, China
Duration: Oct 15 2005Oct 16 2005

Other

Other2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS
CountryChina
CityBeijing
Period10/15/0510/16/05

Fingerprint

Kalman filters
Probability density function
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Xinyu, X., & Li, B. (2005). Rao-blackwellised particle filter for tracking with application in visual surveillance. In Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS (Vol. 2005, pp. 17-24). [1570893] https://doi.org/10.1109/VSPETS.2005.1570893

Rao-blackwellised particle filter for tracking with application in visual surveillance. / Xinyu, Xu; Li, Baoxin.

Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS. Vol. 2005 2005. p. 17-24 1570893.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Xinyu, X & Li, B 2005, Rao-blackwellised particle filter for tracking with application in visual surveillance. in Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS. vol. 2005, 1570893, pp. 17-24, 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS, Beijing, China, 10/15/05. https://doi.org/10.1109/VSPETS.2005.1570893
Xinyu X, Li B. Rao-blackwellised particle filter for tracking with application in visual surveillance. In Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS. Vol. 2005. 2005. p. 17-24. 1570893 https://doi.org/10.1109/VSPETS.2005.1570893
Xinyu, Xu ; Li, Baoxin. / Rao-blackwellised particle filter for tracking with application in visual surveillance. Proceedings - 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS. Vol. 2005 2005. pp. 17-24
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