Visual tracking for UAV using adaptive spatio-temporal regularized correlation filters

A challenge in video tracking is effectively utilizing temporal correlation to navigate the complexity of a tracking scene without imposing additional computational requirements. Different from trackers that rely on online update templates, our methodology introduces a concise and efficient Siamese tracker, which assimilates temporal information via an offline strategy, named SiamGPF. Given the potential for a target to display varied appearances over time, a dependence solely on static template information proves inadequate for addressing extreme deformations and occlusions. Accordingly, we integrate the historical state of the target as pseudopriori information into our framework, using global and local cross-correlation to fuse features accurately. Furthermore, we introduce a state fusion module to enhance the interplay of information across disparate temporal points of the target. In the inference phase, we implement a cost-effective prior frame dynamic selector, adept at eliminating low-quality target information, thereby substantially augmenting the robustness of the tracker. Our tracker exhibits competitive performance across prominent tracking benchmark datasets (OTB100, LaSOT, LaSOT Ext , UAV123, NFS, TC128, GOT-10k, and VOT2016), with the official GOT-10k server evaluation results demonstrating an impressive operational speed of 212 FPS.

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Visual tracking for UAV using adaptive spatio-temporal regularized correlation filters

Cited by 3 publications

References 65 publications

Color-saliency-aware correlation filters with approximate affine transform for visual tracking

Color-saliency-aware correlation filters with approximate affine transform for visual tracking

UAV target following in complex occluded environments with adaptive multi-modal fusion

SiamGPF: temporal correlation-based visual tracking

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