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 multistage 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 intraclass variations while maintaining interclass 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.

中文翻译：

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用于判别跟踪的渐进式多阶段学习

视觉跟踪通常作为判别学习问题来解决，通常需要高质量的样本来进行在线模型适应。评估从先前预测中收集的训练样本并根据质量选择样本来训练模型是一个关键且具有挑战性的问题。为了解决上述问题，我们提出了一种联合判别学习方案，该方案采用渐进式多阶段样本选择优化策略来实现鲁棒的视觉跟踪。所提出的方案提出了一种新颖的时间加权和检测引导的自定进度学习策略，用于轻松进行样本选择，能够容忍相对较大的类内变化，同时保持类间可分性。这种自定进度的学习策略与判别式跟踪过程一起进行了联合优化，从而产生了稳健的跟踪结果。基准数据集上的实验证明了所提出的学习框架的有效性。