基于区域预估与自适应分类的视觉跟踪算法
ing at the problems of target deformation, partial occlusion, and out-of-plane rotation in visual tracking, we propose a visual tracking method based on region estimation and adaptive classification. The method is based on tracking-learning-detection framework. Firstly, we use the Mean-Shift algorithm to realize the tracking, and the tracker is closely connected with the detector. The correction module determines whether the detector needs to be updated online or not. Secondly, we use the Kalman filter to estimate the potential location of the target in order to avoid cumbersome global scanning. Finally, the proposed adaptive variance classifier dynamically adjusts the classifier parameters, enhances the flexibility of the classifier, and improves robustness. Experiments perform on the OTB-2013 evaluation benchmark show that the robustness and accuracy of the proposed algorithm are better than those of contrastive algorithms.
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