  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.
