Motion-Aware Correlation Filters for Online Visual Tracking

Yihong Zhang; Yijin Yang; Wuneng Zhou; Lifeng Shi; Demin Li

doi:10.3390/s18113937

journal article Open Access Nov 14, 2018

Motion-Aware Correlation Filters for Online Visual Tracking

Yihong Zhang

Yijin Yang

Wuneng Zhou

Lifeng Shi Demin Li

Sensors Vol. 18 No. 11 pp. 3937 · MDPI AG

View at Publisher Save 10.3390/s18113937

Abstract

The discriminative correlation filters-based methods struggle deal with the problem of fast motion and heavy occlusion, the problem can severely degrade the performance of trackers, ultimately leading to tracking failures. In this paper, a novel Motion-Aware Correlation Filters (MACF) framework is proposed for online visual object tracking, where a motion-aware strategy based on joint instantaneous motion estimation Kalman filters is integrated into the Discriminative Correlation Filters (DCFs). The proposed motion-aware strategy is used to predict the possible region and scale of the target in the current frame by utilizing the previous estimated 3D motion information. Obviously, this strategy can prevent model drift caused by fast motion. On the base of the predicted region and scale, the MACF detects the position and scale of the target by using the DCFs-based method in the current frame. Furthermore, an adaptive model updating strategy is proposed to address the problem of corrupted models caused by occlusions, where the learning rate is determined by the confidence of the response map. The extensive experiments on popular Object Tracking Benchmark OTB-100, OTB-50 and unmanned aerial vehicles (UAV) video have demonstrated that the proposed MACF tracker performs better than most of the state-of-the-art trackers and achieves a high real-time performance. In addition, the proposed approach can be integrated easily and flexibly into other visual tracking algorithms.

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Published: Nov 14, 2018
Vol/Issue: 18(11)
Pages: 3937
License: View

Authors

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Yihong Zhang