their new bounding box found in frame $t+\delta$. If a person is no longer visible, as they either walked outside of the frame or are being occluded by other people or objects, then their track is [**terminated**]{}. Next, we [**localize**]{} people that are visible at time $t+\delta$, but that were not present at time $t$. When a new person is found, they are either assigned to a newly [**initiated**]{} track or to a track that was terminated earlier ([**reinstated**]{}).

Differently from the other methods in the literature  [@zhang2008global; @berclaz2011multiple; @zamir2012gmcp; @kim2015multiple; @tang2017multiple; @henschel2017improvements; @ristani2018features; @keuper2018motion; @sheng2018heterogeneous; @xu2019spatial; @xu2019train], we propose the first framework that can perform all these tasks in a single, unified network architecture. Our Siamese Track-RCNN consists of three branches: track, detect and re-identify (fig. \[figure:siamese\_track\_rcnn\]). (i) The track branch is responsible for tracking existing tracks from frame $t$ to frame $t+\delta$. At a high level, it searches for that same person instance at frame $t+\delta$ in a window around the location where they were previously observed at frame $t$. As long as the person is visible in the search region, the track branch should produce a high score $\hat{v}$ as well as the regressed location $\hat{m}$ to 