Year: 2026
Type: article
Abstract: Most Transformer-based tracking methods overemphasize tracking accuracy while neglecting tracking efficiency. To address this issue, a lightweight object tracking method based on a hierarchical pruning strategy is proposed. First, an activation module is introduced to adaptively adjust the attention layers of the backbone network, avoiding unnecessary computations of attention layers. Then, based on the demands at different stages of the network, the standard attention mechanism is improved and divided into hybrid attention and cross attention, further reducing redundant computations. Experimental results on multiple benchmark datasets demonstrate that PSTrack achieves a tracking speed of 216 FPS on GPU and 48 FPS on CPU while maintaining competitive accuracy, with an AO of 68.7% on GOT-10k and an AUC of 66.1% on LaSOT. The proposed tracking method exhibits strong competitiveness in both quantitative and qualitative evaluations.
Source: Electronics
Authors: Peiting Gu, Detian Huang, Hang Liu, Xintong Li
Institutions: Quanzhou Normal University, Huaqiao University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7124449270
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