Towards Sequence-Level Training for Visual Tracking

• URL: http://arxiv.org/abs/2208.05810v1
• Date: Thu, 11 Aug 2022 13:15:36 GMT
• Title: Towards Sequence-Level Training for Visual Tracking
• Authors: Minji Kim, Seungkwan Lee, Jungseul Ok, Bohyung Han, Minsu Cho
• Abstract summary: This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning. Four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training.
• Score: 60.95799261482857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the extensive adoption of machine learning on the task of visual object tracking, recent learning-based approaches have largely overlooked the fact that visual tracking is a sequence-level task in its nature; they rely heavily on frame-level training, which inevitably induces inconsistency between training and testing in terms of both data distributions and task objectives. This work introduces a sequence-level training strategy for visual tracking based on reinforcement learning and discusses how a sequence-level design of data sampling, learning objectives, and data augmentation can improve the accuracy and robustness of tracking algorithms. Our experiments on standard benchmarks including LaSOT, TrackingNet, and GOT-10k demonstrate that four representative tracking models, SiamRPN++, SiamAttn, TransT, and TrDiMP, consistently improve by incorporating the proposed methods in training without modifying architectures.

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