A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking

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A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking

Qing Cai; Huiying Liu; Yiming Qian; Sanping Zhou; Jinjun Wang; Yee-Hong Yang

Most existing trackers use bounding boxes for object tracking. However, the background contained in the bounding box inevitably decreases the accuracy of the target model, which affects the performance of the tracker and is particularly pronounced for non-rigid objects. To address the above issue, this paper proposes a novel hybrid level set model, which can robustly address the issue of topology changing, occlusions and abrupt motion in non-rigid object tracking by accurately tracking the object contour. In particular, an appearance model is first obtained by repeatedly training and relabeling the initial labeled frame using competing one-class SVMs. Then, by integrating the trained appearance model, an edge detector and image spatial information into the level set model, a new hybrid level set model is presented, which accurately locates the object contour and feeds back to the competing one-class SVMs to update the appearance model of the next frame. In addition, a motion model is defined to predict the accurate location of the object when occlusion and abrupt motion occur in the next frame. Finally, the experimental results on state-of-the-art benchmarks demonstrate the feasibility and effectiveness of the proposed model and the superiority of the proposed method over existing trackers in terms of accuracy and robustness.

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