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Occlusion is a challenging problem in visual object tracking. Most state-of-the-art trackers may learn the appearance of the occluding target when it becomes occluded by other objects in the scene. This paper proposes a novel approach of detecting occlusion by dividing the target into several patches and computing the peak-to-sidelobe ratio of every response map. Furthermore, our method can calculate...
Feature extractor plays an important role in visual tracking due to the changing appearance of the object. In this paper, we propose a novel approach in correlation filter framework, which decomposes the task of tracking into translation and scale estimation. We employ two correlation filters with hierarchical convolutional features to estimate the translation. Furthermore, we use a discriminative...
Correlation filter-based trackers achieve very good performance in visual tracking, but the traditional correlation tracking methods failed in mining the color information of the image sequence. To solve this problem, we propose a novel and robust scale adaptive tracker combined with color attributes in correlation filter framework, which extracts not only gray but also color information as the feature...
Robust scale calculation is a challenging problem in visual object tracking. Most state-of-the-art trackers fail to handle large scale variations in complex image sequences. This paper propose a novel approach for robust scale calculation in a tracking-by-detection framework. The proposed approach divides the target into four patches and computes the scale factor by finding the maximum response position...
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