In object tracking, the traditional correlation filtering algorithm is unable to perceive the change of scale aspect ratio for moving targets, and it is easily affected by a complex environment, resulting in tracking failure. Therefore, a spatial-temporal regularized correlation filtering algorithm with adaptive aspect ratio(AAR-SRCF) was proposed. Firstly, the average peak-to-correlation energy (APCE) and peak score were used as references to weigh and fuse each feature response map to achieve accurate results. Additionally, a set of novel one-dimensional boundary filters were presented, integrating near-orthogonality and spatial regularization. These filters can adaptively detect changes in the target scale and aspect ratio by precisely locating the boundaries of the target's bounding box. Moreover, spatial regularization effectively mitigated the negative impact of the boundary effect for boundary filters. Finally, the learning rate of each boundary filter was adjusted separately according to the peak-to-sidelobe ratio (PSR) to prevent the model from degradation. Through extensive experiments on OTB datasets, the proposed algorithm shows excellent tracking performance, achieving better results than other excellent algorithms in each challenge attribute.
维普
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