AD-VAT+: An Asymmetric Dueling Mechanism for Learning and Understanding Visual Active Tracking

Visual Active Tracking (VAT) aims at following a target object by autonomously controlling the motion system of a tracker given visual observations. To learn a robust tracker for VAT, in this article, we propose a novel adversarial reinforcement learning (RL) method which adopts an Asymmetric Dueling mechanism, referred to as AD-VAT. In the mechanism, the tracker and target, viewed as two learnable agents, are opponents and can mutually enhance each other during the dueling/competition: i.e., the tracker intends to lockup the target, while the target tries to escape from the tracker. The dueling is asymmetric in that the target is additionally fed with the tracker's observation and action, and learns to predict the tracker's reward as an auxiliary task. Such an asymmetric dueling mechanism produces a stronger target, which in turn induces a more robust tracker. To improve the performance of the tracker in the case of challenging scenarios such as obstacles, we employ more advanced environment augmentation technique and two-stage training strategies, termed as AD-VAT+. For a better understanding of the asymmetric dueling mechanism, we also analyze the target's behaviors as the training proceeds and visualize the latent space of the tracker. The experimental results, in both 2D and 3D environments, demonstrate that the proposed method leads to a faster convergence in training and yields more robust tracking behaviors in different testing scenarios. The potential of the active tracker is also shown in real-world videos.