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| Reliable CAMShift Tracking Based on Watershed Segmentation and Kernel Restriction |
| Keli HU, Yuzhang GU, Cheng ZHANG, Yingguan WANG |
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Abstract Traditional Continuous Adaptive Mean Shift (CAMShift) will become unstable when similar objects are presented in background or occlusion happens. To overcome this problem, we present a modified back-projection method with the help of watershed segmentation and Epanechnikov kernel restriction. Both the ratio of watershed regions contributing to the proposed back-projection and the location of the kernel are decided by watershed seeds belonging to the object, which called object seeds here. In addition, all the object seeds are adaptively updated during the tracking process. Experimental results demonstrate that the proposed algorithm is more robust compared to the traditional CAMShift.
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