the model to become uncertain when the object is occluded or there are multiple of the same type of object in close proximity; (ii) it only performs short-term tracking, as it cannot re-identify the target object after it temporally disappears. Bergmann et al. tackled the second limitation by enhancing Tracktor with a re-id network (i.e., Tracktor++), which led to better performance, but at the cost of efficiency, partially defeating the benefits of Tracktor.

In this paper we propose a novel MOT approach: [*Siamese Track-RCNN*]{}. Differently from the previous works, we address all the aforementioned problems in a single, unified network architecture. Our approach is both efficient and accurate. Our proposed system performs both detection and association in a single forward pass of its network, which consists of a backbone shared among three branches: detection, tracking and re-identification. This design has several advantages: low computational cost, low memory footprint and higher accuracy, as all these branches can benefit from each other. At a high level, the detection branch localizes people entering the field of view, the track branch follows them into the proceeding frames and the re-id branch is responsible for associations across longer periods of time.

We conduct experiments on 