alone is not robust to short-term changes, especially in the presence of partial occlusions. Instead, we propose to enrich the popular Faster-RCNN [@ren2015faster] object detector (we track bounding boxes, not masks) with both a re-id branch and a tracking branch. We show that these are highly complementary and their combination leads to SOTA MOT performance.

Siamese Track-RCNN {#sec:architecture}
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![*Siamese Track-RCNN unifies detection, tracking and re-id in a single network architecture. Importantly, these features share the same backbone, which results in low computation and efficient runtime.* []{data-label="figure:siamese_track_rcnn"}](Figs/schema.pdf){width="100.00000%"}

Our tracking system follows an inference pipeline similar to other tracking-by-detection systems, but it does so from cues generated by a single network. We first give a high-level description of our online tracking inference with an eye toward identifying the cues that need to be generated by our approach and then describe the structure of our unified tracking network in detail.

Our Siamese Track-RCNN tracking method operates [*online*]{}. As such, it tracks people forward in time by only propagating information from the past. It functions by looking at pairs of frames: {$t$, $t+\delta$}, where people in frame $t$ are searched for in frame $t+\delta$. If they are found, their track is [**continued**]{} with 