input instead of a single image. To accommodate for this difference, we average the detection loss $\ell_{detect}$ from both frames while training our detection branch.

Track branch {#track_branch}
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The task of tracking a particular person instance over time is particularly challenging for three reasons: (i) the appearance of the person can change rapidly over time due to pose changes, occlusion and motion blur; (ii) the location of a person can change quickly due to fast object and camera motion; (iii) tracks of different people can intersect when the individuals cross each other. To overcome the first two limitations, we propose a Siamese-based solution that is robust to appearance changes and fast motion. For the third, instead, we rely on the re-id branch to help distinguish the two people that are intersecting. Similar to Siamese-based single object tracker in the literature (SOT) [@held2016learning; @li2018high; @zhu2018distractor; @li2019siamrpn++; @wang2019fast], given two frames $t$ and $t+\delta$, we model this problem as matching (searching) a target template from $t$ within a larger contextual region in $t+\delta$. More specifically, given an ongoing track $\mathcal{T}$ of a person bounding box defined as $target^t=[x^t, y^t, w^t, h^t]$ at time $t$, our branch attempts to localize the person 