  In active visual tracking, it is notoriously difficult when distracting
objects appear, as distractors often mislead the tracker by occluding the
target or bringing a confusing appearance. To address this issue, we propose a
mixed cooperative-competitive multi-agent game, where a target and multiple
distractors form a collaborative team to play against a tracker and make it
fail to follow. Through learning in our game, diverse distracting behaviors of
the distractors naturally emerge, thereby exposing the tracker's weakness,
which helps enhance the distraction-robustness of the tracker. For effective
learning, we then present a bunch of practical methods, including a reward
function for distractors, a cross-modal teacher-student learning strategy, and
a recurrent attention mechanism for the tracker. The experimental results show
that our tracker performs desired distraction-robust active visual tracking and
can be well generalized to unseen environments. We also show that the
multi-agent game can be used to adversarially test the robustness of trackers.
