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arXiv · 2203.05328

Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking

Boyu Chen·Peixia Li·Lei Bai·Lei Qiao·Qiuhong Shen·Bo Li·Weihao Gan·Wei Wu·Wanli Ouyang
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Abstract

Exploiting a general-purpose neural architecture to replace hand-wired designs or inductive biases has recently drawn extensive interest. However, existing tracking approaches rely on customized sub-modules and need prior knowledge for architecture selection, hindering the tracking development in a more general system. This paper presents a Simplified Tracking architecture (SimTrack) by leveraging a transformer backbone for joint feature extraction and interaction. Unlike existing Siamese trackers, we serialize the input images and concatenate them directly before the one-branch backbone. Feature interaction in the backbone helps to remove well-designed interaction modules and produce a more efficient and effective framework. To reduce the information loss from down-sampling in vision transformers, we further propose a foveal window strategy, providing more diverse input patches with acceptable computational costs. Our SimTrack improves the baseline with 2.5%/2.6% AUC gains on LaSOT/TNL2K and gets results competitive with other specialized tracking algorithms without bells and whistles.

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Boyu Chen, Peixia Li, Lei Bai, Lei Qiao, Qiuhong Shen, Bo Li, Weihao Gan, Wei Wu, Wanli Ouyang. 2022-07-15. Backbone is All Your Need: A Simplified Architecture for Visual Object Tracking. https://arxiv.org/abs/2203.05328

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