Abstract
Considering the problems of motion blur, partial occlusion and fast motion in target tracking, a target tracking method based on adaptive structured sparse representation with attention is proposed. Under the framework of particle filtering, the performance of high-quality templates is enhanced through an attention mechanism. Structure sparseness is used to build candidate target sets and sparse models between candidate samples and local patches of target templates. Combined with the sparse residual method, reconstruction error is reduced. After optimally solving the model, the particle with the highest similarity is selected as the prediction target. The most appropriate scale is selected according to the multiscale factor method. Experiments show that the proposed algorithm has a strong performance when dealing with motion blur, fast motion, partial occlusion.
Highlights
Target tracking automatically locates a target in subsequent frames according to the state of a known target in the initial image frame
According to the methods established by the target observation model, the target tracking methods [1]–[5] can be divided into two categories: discriminative methods and generative methods
The generative method represents the target through the learned appearance model and selects the candidate patch with the smallest reconstruction error as the target area of the frame
Summary
Target tracking automatically locates a target in subsequent frames according to the state of a known target in the initial image frame. In [17], [22], the template T represents each target candidate area xi by means of sparse linear combination and uses a dynamic update method to describe the appearance model of the target. This type of method has achieved good results, when it encounters occlusion situations, the tracking efficiency decreases sharply because of the global sparse model. Reference [16] represented local patches in candidate regions as linear combinations of dictionaries by solving the l1 minimization problem Most of these methods are based on static local sparseness.
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