Illumination Invariant Object Tracking with Incremental Subspace Learning
In this paper, we present an efficient and robust sub-space learning based object tracking algorithM with special illumination handling. Illumination variances pose a great challenge to most of object tracking algorithms. In this paper, an edge orientation based feature has been proposed and proven to approximately invariant to illumination changes. Besides, we utilize the incremental subspace learning based particle filter framework which is effective to handle various appearance changes. To reduce the amount of computation when the particle number is large, a new layer of preprocessing step has been added to the particle filter framework with the help of edge orientation features. From the experimental results, it is obvious that our proposed algorithm achieves promising performance especially in the scenarios with large illumination changes.
Gang Yu Hongtao Lu
MOE-Microsoft Laboratory for Intelligent Computing and Intelligent Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
131-136
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）