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A Transformer-based visual object tracker via learning immediate appearance change
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DOI：10.1016/j.patcog.2024.110705.png)
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摘要
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Transformer has shown its great strength in visual object tracking due to its effective attention mechanism, but most prevailing transformer-based trackers only explore temporal information frame by frame, thus overlooking the rich context information inherent in videos. To alleviate this problem, we propose a transformer-based tracker via learning immediate appearance change information in videos, called IAC-tracker. The proposed tracker enhances the perception of the immediate motion state to improve the performance of single target tracking. IAC-tracker contains three key components: a spatial information extractor (SIE) with a superior attention mechanism to progressively extract spatial information, a temporal information extractor (TIE) with designed temporal attention mechanism to progressively learn target immediate appearance change, and novel spatial-temporal context enhanced fusion module integrating the information from SIE and TIE to prepare for the final prediction head. Comparison experiments with state-of-the-art trackers on six challenging datasets demonstrate the superior performance of IAC-tracker with real-time running speed.
Keyword:
Transformer
Attention mechanism
Visual object tracking
Appearance change
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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