Abstract
Discriminative correlation filters based algorithms have attracted extensive attention due to their strong tracking capability. However, object tracking still faces many challenges due to object appearance variations, background clutter, occlusion, plane rotation, etc. In this paper, to better express the object, multiple features are integrated to make full use of the advantage of different features. Furthermore, the adaptive eigen-decomposition and reconstruction ("eigenmodes") method is applied to carry out the integrated-feature decomposition, and optimal expression of the object is established through simple eigen-relationships. It has proved experimentally that the predicted value after eigenmodes is closer to the groundtruth than before. Furthermore, to solve the tracking failure caused by interfering objects or background clutters and improve the tracking accuracy, the average peak-correlation energy (APCE) method is utilized as an optimized update strategy in this paper. A large number of experimental results on the known reference datasets indicate that our algorithm has good performance compared to the existing tracking methods.
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