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Aiming at the existing problems that object tracking algorithm fails to track under the influence of occlusion conditions, the paper has improved the Kernel Correlation Filter algorithm. Firstly, the occlusion condition has been added to the Kernel Correlation Filter algorithm. If there is no occlusion, the Kernel Correlation Filter algorithm has used for object tracking. If there is occlusion, the improved algorithm based on Unscented Rauch--Tung--Striebel Smoother has been used. Secondly, the predicted position of the object has been feedback to the Kernel Correlation Filter algorithm. Finally, the combination of adaptive multi-model has been realized by combining the color histogram with the Kernel Correlation Filter algorithm, and the sparse representation method has been introduced into the training process to heighten the stability of the proposed object tracking algorithm. The experimental results using the proposed method on the OTB-2013 dataset can express that the proposed object tracking algorithm can reduce the occlusion interference in the object tracking process, and ameliorate the accuracy rate and success rate.
Xia et al. (Sat,) studied this question.