Object Tracking Based on a Time-Varying Spatio-Temporal Regularized Correlation Filter With Aberrance Repression

When used for object tracking, the discriminative correlation filter (DCF) is effective, but its performance is often burdened by undesirable boundary effects. Meanwhile, when there is too much background information in training samples of the DCF, it will be easier to learn the area deviating from the tracking object. Further, illumination variation, partial/full occlusion, and appearance variations, render the response map aberrance of the correlation filter (CF) more prone to occur. To overcome these problems, an object tracking model based on a time-varying Spatiotemporal regularized correlation filter with aberrance repression is proposed in this paper. Firstly, by adding a regularized term to the traditional CFs to limit the change rate of the response map generated in the object detection phase, the proposed tracker can obviously repress the aberrance of the response maps; secondly, by adjusting the filter to the object regions suitable for tracking with high confidence scores with a time-varying spatial reliability map, the proposed tracker effectively overcomes the adverse effects caused by the boundary effect; and finally, by introducing a temporal regularized term, the proposed tracker also has superior tracking ability for the partial occluded objects and those with large appearance variations. Significant experiments on the OTB100, VOT2016, TC128, and UAV 123 datasets have revealed that the performance thereof outperformed many state-of-the-art trackers based on DCF and deep-based frameworks in terms of tracking accuracy, tracking success rate, and A-R rank, etc.

Automated summary

This paper proposes an object tracking model based on a time-varying spatiotemporal regularized correlation filter to overcome the boundary effects and aberrance caused by illumination variation, occlusion, and appearance variations. The proposed tracker suppresses the aberrance of the response maps by adding a regularized term and adjusts the filter position using a time-varying spatial reliability map. Experimental results on various datasets demonstrate that the proposed tracker outperforms other state-of-the-art trackers based on DCF and deep-based frameworks in terms of tracking accuracy and success rate.