A Novel Object Tracking Algorithm Based on Particle Filtering on the Affine Group
In this paper, a novel approach based on particle filtering on the affine group is introduced for object tracking. We firstly use Scale invariant feature transform (SIFT) to extract corresponding feature points between two successive frames. The affine parameters for pose estimation from the corresponding feature points can be formed as a solution to Sylvesters equation. Then, we can smoothly estimate the affine parameters within the particle filtering framework. Where the state dynamic is modeled via the first order autoregressive (AR) process on the affine group, at the same time, the optimal state of the tracked object is estimated through the total likelihood function using appearance model and feature model. Experimental results demonstrate the robustness and efficiency of our proposed approach for object tracking.
Sylvesters equation affine group particle filtering object tracking
Zhiyan Xiang Tieyong Cao Jingfeng Pan
Institute of Communications Engineering PLA University of Science and Technology Nanjing, China Institute of Command Automation PLA University of Science and Technology Nanjing, China
国际会议
三峡
英文
2928-2932
2012-05-18（万方平台首次上网日期，不代表论文的发表时间）