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

Abstract

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, factors such as illumination variation, partial/full occlusion of the object, and variations in the object appearance, render the response map aberrance of the correlation filter (CF) more prone to occur. To overcome the aforementioned problems, an object tracking model based on a time-varying Spatio-temporal regularized correlation filter with aberrance repression is proposed in this paper. Firstly, by adding a regularized term to the objective function of 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 although the proposed tracker only uses the histogram of directional gradient (HOG) and color name (CN) features, the performance thereof outperformed many state-ofthe-art trackers based on DCF and deep-based frameworks in terms of tracking accuracy, tracking success rate, and A-R rank, etc.

Analisis Metode Kalman Filter, Particle Filter dan Correlation Filter Untuk Pelacakan Objek

“…Metode Correlation Filter pertama kali diperkenalkan untuk pelacakan objek oleh Bolme, dkk pada tahun 2010 yang biasa dikenal dengan MOSSE [1], [2], [4], [5], [6], [7], [8], [9], [16], [17]. Seiring berjalannya waktu, algoritma Correlation Filter semakin berkembang.…”