Research Projects

Structural Parts Learning and Graph Matching for Visual Tracking
Mar 14, 2014Author:
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Object tracking is one of the foundation problems in computer vision. It is an interesting but challenging problem due to many annoying factors, e.g., pose changes, occlusion, illumination variation, complex backgrounds, large non-rigid motion and so on. In this project, we propose to utilize the adaptive structural parts learning method, to mitigate the influence of the pose changes and large non-rigid motion problem. Furthermore, we propose to utilize the graph model to represent the interactions between different parts to reflect the structure information of the target. Combining the target appearance and structure information, the graph matching method is adopted to infer the size and position variation of the target. Based on the single target appearance modeling and tracking method, we absorb the advantages of both detection and tracking module, i.e., tracking by detection to handle the drift problem and detection by tracking to address the miss detection problem. In this way, we construct the spatio-temporal structure graph to complete the multiple targets tracking problem. The approximate optimal solution to the global energy function with the global structure graph is achieved using the graph based optimization methods.