SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning
This addresses security concerns for person tracking applications like autonomous driving and home security, but it is incremental as it builds on existing methods.
The paper tackles the problem of adversarial attacks on person tracking systems by investigating multi-task learning with human keypoint detection, finding that it consistently makes the SiamRPN tracker harder to attack compared to single-task training.
Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of adversarial attacks raises serious concerns regarding the security and reliability of such techniques. In this work, we study the impact of multi-task learning (MTL) on the adversarial robustness of the widely used SiamRPN tracker, in the context of person tracking. Specifically, we investigate the effect of jointly learning with semantically analogous tasks of person tracking and human keypoint detection. We conduct extensive experiments with more powerful adversarial attacks that can be physically realizable, demonstrating the practical value of our approach. Our empirical study with simulated as well as real-world datasets reveals that training with MTL consistently makes it harder to attack the SiamRPN tracker, compared to typically training only on the single task of person tracking.