It is well known that achieving a robust visual tracking task is quite difficult, since it is easily interfered by scale variation, illumination variation, background clutter, occlusion and so on. Nevertheless, the performance of spatio-temporal context algorithm is remarkable, because the spatial context information of target is effectively employed in this algorithm. However, the capabilities of discriminate target and adjust to scale variation need to promote in complex scene. Furthermore, due to lack of an appropriate target model update strategy, its tracking capability also deteriorates. In the interest of tackling these problems, a multi-scale spatio-temporal context visual tracking algorithm based on target model adaptive update is proposed. Firstly, the histogram of oriented gradient features are adopted to describe the target and its surrounding regions to improve its discriminate ability. Secondly, a multi-scale estimation method is applied to predict the target scale variation. Then, the peak and the average peak to correlation energy of confidence map response are combined to evaluate the visual tracking status. When the status is stable, the current target is expressed in a low rank form and a CUR filter is learned. On the contrary, the CUR filter will be triggered to recapture the target. Finally, the experimental results demonstrate that the robustness of this algorithm is promoted obviously, and its overall performance is better than comparison algorithms.
Visual tracking plays a significant role in computer vision. Although numerous tracking algorithms have shown promising results, target tracking remains a challenging task due to appearance changes caused by deformation, scale variation, and partial occlusion. Part-based methods have great potential in addressing the deformation and partial occlusion issues. Owing to the addition of multiple part trackers, most of these part-based trackers cannot run in real time. Correlation filters have been used in target tracking owing to their high efficiency. However, the correlation filter-based trackers face great problems dealing with occlusion, deformation, and scale variation. To better address the above-mentioned issues, we present a scale adaptive part-based tracking method using multiple correlation filters. Our proposed method utilizes the scale-adaptive tracker for both root and parts. The target location is determined by the responses of root tracker and part trackers collaboratively. To estimate the target scale more precisely, the root scale and each part scale are predicted with the sequential Monte Carlo framework. An adaptive weight joint confidence map is acquired by assigning proper weights to independent confidence maps. Experimental results on the publicly available OTB100 dataset demonstrate that our approach outperforms other state-of-the-art trackers.
Correlation filter, previously used in object detection and recognition assignment within single image, has become a popular approach to visual tracking due to its high efficiency and robustness. Many trackers based on the correlation filter, including Minimum Output Sum of Squared Error (MOSSE), Circulant Structure tracker with Kernels (CSK) and Kernel Correlation Filter (KCF), they simply estimate the translation of a target and provide no insight into the scale variation of a target. But in visual tracking, scale variation is one of the most common challenges and it influences the visual tracking performance in stability and accuracy. Thus, it is necessary to handle the scale variation. In this paper, we present an accurate scale estimation solution with two steps based on the KCF framework in order to tackle the changing of target scale. Meanwhile, besides the original pixel grayscale feature, we integrate the powerful features Histogram of Gradient (HoG) and Color Names (CN) together to further boost the overall visual tracking performance. Finally, the experimental results demonstrate that the proposed method outperforms other state-of-the-art trackers.
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