  Multi-Object Tracking (MOT) has achieved aggressive progress and derived many
excellent deep learning trackers. Meanwhile, most deep learning models are
known to be vulnerable to adversarial examples that are crafted with small
perturbations but could mislead the model prediction. In this work, we observe
that the robustness on the MOT trackers is rarely studied, and it is
challenging to attack the MOT system since its mature association algorithms
are designed to be robust against errors during the tracking. To this end, we
analyze the vulnerability of popular MOT trackers and propose a novel
adversarial attack method called Tracklet-Switch (TraSw) against the complete
tracking pipeline of MOT. The proposed TraSw can fool the advanced deep
pedestrian trackers (i.e., FairMOT and ByteTrack), causing them fail to track
the targets in the subsequent frames by perturbing very few frames. Experiments
on the MOT-Challenge datasets (i.e., 2DMOT15, MOT17, and MOT20) show that TraSw
can achieve an extraordinarily high success attack rate of over 95% by
attacking only four frames on average. To our knowledge, this is the first work
on the adversarial attack against the pedestrian MOT trackers. Code is
available at https://github.com/JHL-HUST/TraSw .
