Application of Multi-Object Tracking with Siamese Track-RCNN to the Human in Events Dataset
Bing Shuai, Andrew Berneshawi, Manchen Wang, Chunhui Liu, Davide Modolo, Xinyu Li, Joseph Tighe · 2020
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 used this design and apply it to the Human in Events dataset.