Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking

Efficiency has been a critical problem in UAV tracking due to limitations in computation resources, battery capacity, and unmanned aerial vehicle maximum load. Although discriminative correlation filters (DCF)-based trackers prevail in this field for their favorable efficiency, some recently proposed lightweight deep learning (DL)-based trackers using model compression demonstrated quite remarkable CPU efficiency as well as precision. Unfortunately, the model compression methods utilized by these works, though simple, are still unable to achieve satisfying tracking precision with higher compression rates. This paper aims to exploit disentangled representation learning with mutual information maximization (DR-MIM) to further improve DL-based trackers' precision and efficiency for UAV tracking. The proposed disentangled representation separates the feature into an identity-related and an identity-unrelated features. Only the latter is used, which enhances the effectiveness of the feature representation for subsequent classification and regression tasks. Extensive experiments on four UAV benchmarks, including UAV123@10fps, DTB70, UAVDT and VisDrone2018, show that our DR-MIM tracker significantly outperforms state-of-the-art UAV tracking methods

An information-theoretic on-line update principle for perception-action coupling

Inspired by findings of sensorimotor coupling in humans and animals, there has recently been a growing interest in the interaction between action and perception in robotic systems [Bogh et al., 2016]. Here we consider perception and action as two serial information channels with limited information-processing capacity. We follow [Genewein et al., 2015] and formulate a constrained optimization problem that maximizes utility under limited information-processing capacity in the two channels. As a solution we obtain an optimal perceptual channel and an optimal action channel that are coupled such that perceptual information is optimized with respect to downstream processing in the action module. The main novelty of this study is that we propose an online optimization procedure to find bounded-optimal perception and action channels in parameterized serial perception-action systems. In particular, we implement the perceptual channel as a multi-layer neural network and the action channel as a multinomial distribution. We illustrate our method in a NAO robot simulator with a simplified cup lifting task.Comment: 8 pages, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS