Hybrid Contrastive Transformer for Visual Tracking
Keywords: visual tracking, contrastive learning, hybrid feature, redundant pruning
Abstract: Visual object tracking is a research hotspot in the field of computer vision, and has been widely applied in video surveillance, human-computer interaction, unmanned driving and other fields. At present, the object trackers based on Transformer have good performance, but they still face the challenge of confusing target and background in the feature extraction process. To address this issue, we propose a Hybrid Contrastive Transformer Tracker (HCTrack) in this paper, which combines contrastive learning to improve the ability of distinguishing the target and the background in video. Furthermore, a hybrid feature interaction module is presented to realize multi-level information exchange between the features of template and search regions and capture the target-related semantic information of the search frames comprehensively. Additionally, we design a redundant information pruning module to adaptively eliminate the redundant backgrounds according to the global scene information, thereby reducing the interference of the background to the target feature. HCTrack achieves superior tracking accuracy on the GOT-10k and TrackingNet datasets compared to other state-of-the-art trackers, while maintaining fast inference speed, as the contrastive learning is only implemented during training model.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 10035
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