In this paper, we propose a novel visual object tracking method using a part based appearance model. First, a local kernel feature is developed to encode edge information of patches. Next, bounding box of the target is divided into multiple parts. Then, each part uses correlation filter based tracking to predict position in the next frame. The matrix cosine similarity is utilized to measure reliabilities of the patches. Finally, optimal target location is predicted via maximizing the likelihood, which is obtained by adaptively fusing the reliable patches locations. Experimental results illustrate that our algorithm outperforms state-of-the-art tracking methods significantly in terms of accuracy and robustness.

中文翻译：

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通过基于零件的模型进行稳健的视觉跟踪

在本文中，我们提出了一种使用基于部件的外观模型的新型视觉对象跟踪方法。首先，开发了一个局部内核特征来编码补丁的边缘信息。接下来，目标的边界框被分成多个部分。然后，每个部分使用基于相关滤波器的跟踪来预测下一帧中的位置。矩阵余弦相似度用于测量补丁的可靠性。最后，通过自适应融合可靠补丁位置获得的似然最大化来预测最佳目标位置。实验结果表明，我们的算法在准确性和鲁棒性方面明显优于最先进的跟踪方法。