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Robust Visual Tracking with Motion-Aware and Automatic Temporal Regularization

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Abstract

Discriminative correlation filters (DCF) have significantly advanced visual target tracking. However, most DCF-based trackers suffer from various challenges such as occlusion, rotation, and background clutters. Therefore, we propose a novel visual tracking framework, which introduces a motion-aware strategy and automatic temporal regularization mechanism into the spatial-temporal regularization correlation filter (STRCF) to improve tracking stability. Specifically, the motion-aware strategy based on the optimal Kalman filter (KF) is used to estimate the possible state of the target for overcoming the instability problem in complex environments. Furthermore, a novel automatic temporal regularization mechanism is proposed to solve the problem of target drift due to overhigh temporal penalty. Compared with STRCF, our method obtains AUC gains of 5.86%, 2.60%, 3.82%, 4.95%, 3.55%, and 1.90% for the occlusion, motion blur, in-plane Rotation, out-of-plane rotation, background clutters, and scale variation attributes on the OTB-2015 datasets, respectively. Extensive experiment results on OTB-2013, DTB-70, and UAV-123 datasets have proven the effectiveness and stability of our method.

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  1. Heng Yuan and Huan Qi contributed equally to this work.

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  1. School of Software, Liaoning Technical University, Huludao, 125105, China

    Heng Yuan & Huan Qi

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  1. Heng Yuan
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Correspondence to Huan Qi.

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Yuan, H., Qi, H. Robust Visual Tracking with Motion-Aware and Automatic Temporal Regularization. Neural Process Lett 55, 3471–3488 (2023). https://doi.org/10.1007/s11063-022-11018-x

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