---
abstract: '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.'
author:
- Bing Shuai
- 'Andrew G. Berneshawi'
- Davide Modolo
- Joseph Tighe
bibliography:
- 'eccv2020\_bib.bib'
title:
- 'Multiple Object Tracking with Siamese Track-RCNN'
- Appendix
---

Introduction
============

Multi-object tracking (MOT) deals with the problem of localizing and tracking object instances over entire video sequences. Recently, the most successful approaches in the literature are based on the “tracking-by-detection” paradigm, which consists of two major components: object detection and association. 