Abstract
To solve the problem of moving object loss and inaccurate localization caused by the rapid change of object appearance, partial occlusion, and scale change in the tracking process, the channel response constraints correlation filter was proposed, i.e., CRCCF. Firstly, the channel self-optimizing regularization is introduced on the background-aware correlation filter (BACF), reducing the interference of redundant channel information to adapt to the complex environment. Then, the different response error regularization was introduced, the work used differences of adjacent frames response figures to learn the object appearance changes, which can smooth the volatility of the response figure to alleviate the drift problem model. And the Alternating Direction Method of Multipliers (ADMM) is used to optimize the model. Finally, a large number of experiments were carried out on OTB50, and OTB100 datasets to verify that the proposed algorithm has better performance under the conditions of fast motion, background clutter, and motion blur.
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