| Summary: | Visual object tracking is a challenging task when the object appearance changes caused by the scale variation and the occlusion. In this paper, an object tracking algorithm is proposed which is capable of dealing with the case that the scale variation and the occlusion occur simultaneously. A kernelized correlation filter (KCF) is first learned to obtain the correlation response, whose maximum value denotes the optimal object location. In order to represent the sample better, the convolutional features are extracted from a pre-trained convolutional neural networks (CNNs). Then, the strategy of scale adaption is used to estimate the object scale during the tracking process. Subsequently, a novel re-detection model is proposed by using a support vector machine (SVM) classifier to re-find the object when the occlusion occurs. The comparison experiments are implemented on the object tracking benchmark (OTB) and the results demonstrate that the proposed tracking algorithm outperforms other state-of-the-art ones in terms of precision and success rate.
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