Progressive Multi-Stage Learning for Discriminative Tracking

• URL: http://arxiv.org/abs/2004.00255v1
• Date: Wed, 1 Apr 2020 07:01:30 GMT
• Title: Progressive Multi-Stage Learning for Discriminative Tracking
• Authors: Weichao Li, Xi Li, Omar Elfarouk Bourahla, Fuxian Huang, Fei Wu, Wei Liu, Zhiheng Wang, and Hongmin Liu
• Abstract summary: We propose a joint discriminative learning scheme with the progressive multi-stage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.
• Score: 25.94944743206374
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation. It is a critical and challenging problem to evaluate the training samples collected from previous predictions and employ sample selection by their quality to train the model. To tackle the above problem, we propose a joint discriminative learning scheme with the progressive multi-stage optimization policy of sample selection for robust visual tracking. The proposed scheme presents a novel time-weighted and detection-guided self-paced learning strategy for easy-to-hard sample selection, which is capable of tolerating relatively large intra-class variations while maintaining inter-class separability. Such a self-paced learning strategy is jointly optimized in conjunction with the discriminative tracking process, resulting in robust tracking results. Experiments on the benchmark datasets demonstrate the effectiveness of the proposed learning framework.

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