Correlation filter learning with dual-modal motion fusion and total variation for UAV tracking

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Abstract

Unmanned aerial vehicle (UAV) visual tracking is an important task in low-altitude perception and aerial monitoring, where trackers must handle fast camera motion, scale variation, occlusion, background clutter, and limited onboard computational resources. These challenges can easily lead to tracking drift or failure. To address these issues, we propose a correlation filter tracker named correlation filter learning with dual-modal motion fusion and total variation for UAV tracking (CFLDT). First, the search region is updated using the dual-modal motion fusion (DMMF), and features are then extracted from the calibrated area. This process integrates local motion cues and correlation response information to improve target localization under complex UAV motion. In addition, a multi-response spatial penalty matrix (MRSP) is introduced to dynamically constrain the filter, while the environment-aware regularization term is optimized through the total variation constraint module (TVCM). Finally, both the MRSP and the environment-aware regularization term are incorporated into the filter training process, yielding a more discriminative model for distinguishing the target from the background. Extensive comparative experiments on multiple UAV tracking datasets demonstrate that CFLDT achieves competitive tracking accuracy, robustness, and real-time performance.

Keywords

UAV target tracking / total variation / correlation filtering

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