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.
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.
In the traditional bootstrap particle filter, the state transition density is used as the importance sampling function, which brings some problems such as particle degradation and poor tracking accuracy. In this paper, the posterior probability is used as the importance sampling function and its estimation method is proposed. By means of cubature information filtering and Gating technique, the mean and variance of the importance sampling function are estimated, and the importance sampling function is designed. The improved particle filter method is used to estimate the number of targets and the number of targets in the nonlinear situation. The simulation results show that the proposed algorithm has the advantages of high estimation accuracy and good stability in the nonlinear multi-target tracking scenario.
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