论文标题
带有蒸馏和加固型号的跟踪轨迹
Tracking-by-Trackers with a Distilled and Reinforced Model
论文作者
论文摘要
视觉对象跟踪通常是通过在快速处理算法，准确的在线适应方法和跟踪器融合的快速处理算法上独立推理来解决的。在本文中，我们通过提出一种新颖的跟踪方法来统一此类目标，该方法利用其他视觉跟踪器，离线和在线。紧凑的学生模型通过知识蒸馏和强化学习的结合而受到培训。首先允许转移和压缩其他跟踪器的跟踪知识。第二种使得学习评估措施，然后在线利用这些措施。学习后，学生最终可以用来构建（i）一个非常快速的单声跟踪器，（ii）具有简单有效的在线适应机制的跟踪器，（iii）进行其他跟踪器融合的跟踪器。广泛的验证表明，拟议的算法与实时最先进的跟踪器竞争。
Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we unify such goals by proposing a novel tracking methodology that takes advantage of other visual trackers, offline and online. A compact student model is trained via the marriage of knowledge distillation and reinforcement learning. The first allows to transfer and compress tracking knowledge of other trackers. The second enables the learning of evaluation measures which are then exploited online. After learning, the student can be ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker with a simple and effective online adaptation mechanism, (iii) a tracker that performs fusion of other trackers. Extensive validation shows that the proposed algorithms compete with real-time state-of-the-art trackers.
