  Multi-object tracking systems often consist of a combination of a detector, a
short term linker, a re-identification feature extractor and a solver that
takes the output from these separate components and makes a final prediction.
Differently, this work aims to unify all these in a single tracking system.
Towards this, we propose Siamese Track-RCNN, a two stage detect-and-track
framework which consists of three functional branches: (1) the detection branch
localizes object instances; (2) the Siamese-based track branch estimates the
object motion and (3) the object re-identification branch re-activates the
previously terminated tracks when they re-emerge. We test our tracking system
on two popular datasets of the MOTChallenge. Siamese Track-RCNN achieves
significantly higher results than the state-of-the-art, while also being much
more efficient, thanks to its unified design.
