Object tracking is a classical problem in computer vision. Existing approaches often use feature descriptors from single cues to build tracking model. This make them fragile under varying illumination conditions and object appearances. Recently, Quang et al have reported an object tracking method that combines different features using graphical model. This method allows weighted contribution from each feature to the final target shift, which permits quick adaptation of the trackers to the environment change and occlusions. The proposed research will extend the object tracking method from Quang et al.
1. Implement the object tracking method in the following paper: Quang Nguyen, Antonio Robles-Kelly, and Jun Zhou. "A Graph Embedding Approach to Feature Combination for Object Tracking". Proceedings of The Ninth Asian Conference on Computer Vision (ACCV'09), , Xi'an, China, 2009. 2. Implement several alternative object trackers in the above paper. 3. If possible, develop a new the feature combination method.
Experiences with Matlab and C/C++. Basic knowledge in Computer Vision.
Experiences in research and development in video processing and related topics in computer vision.