An Improved Particle Filter Approach for Real-time Pedestrian Tracking in Surveillance Video
Yaowen Guan, Xiaoou Chen, Yuqian Wu, Deshun Yang
Available Online June 2013.
- 10.2991/icista.2013.35How to use a DOI?
- Pedestrian tracking, particle filter, surveillance video
This paper presents a method for pedestrian tracking in surveillance video, and the method is based on an improved particle filter. In our algorithm, the dynamics is modeled as a second-order autoregressive process. And for the observation model, color histogram features are used for likelihood measure. The proposed color histogram method is operated on a sub-region of the target region and we explore how the background subtraction process affects the color histogram model. We further adopt rectangle filters and pixel-difference cues in the observation model to overcome the limitation of individual cue. Experiments show that the method yields better tracking performance with the improved observation model.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yaowen Guan AU - Xiaoou Chen AU - Yuqian Wu AU - Deshun Yang PY - 2013/06 DA - 2013/06 TI - An Improved Particle Filter Approach for Real-time Pedestrian Tracking in Surveillance Video BT - Proceedings of the 2013 International Conference on Information Science and Technology Applications (ICISTA-2013) PB - Atlantis Press SP - 173 EP - 177 SN - 1951-6851 UR - https://doi.org/10.2991/icista.2013.35 DO - 10.2991/icista.2013.35 ID - Guan2013/06 ER -