A novel bagged particle filter for object tracking

Authors

Haozhi Huang, Macau University of Science and Technology
Yanyan Liang, Macau University of Science and Technology
Ah Chung Tsoi, University of WollongongFollow
S. Lo, University of Hong KongFollow
Alex Leung, Macau University of Science and Technology

RIS ID

111921

Publication Details

Huang, H., Liang, Y., Tsoi, A., Lo, S. & Leung, A. (2016). A novel bagged particle filter for object tracking. Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry (VRCAI'16) (pp. 331-338). New York: ACM.

Abstract

2016 ACM.In this paper, we propose a novel bagged particle filter framework to filtering the noise information from object trackers using generative model as well as the discriminative model. The framework makes use of objectness measurement for modeling observation likelihood and two powerful object detectors: the real-time L1 tracker and the TLD tracker combined to bagged trackers. By maxmazing the posterior of the proposed inference, inaccuracy information is filtered and more accuracy result from varying samples returned by different trackers is provided by the bagged particle filter. The experiment results suggest that the proposed particle filter is effective in combing the complementary nature of either the sparse tracking approach and the discriminative learning approach.