Object Tracking and Reidentification with FairMOT

Arguably, the most crucial task of a Deep Learning based Multiple Object Tracking (MOT) is not to identify an object, but to re-identify it after occlusion. There are a plethora of trackers available to use, but not all of them have a good re-identification pipeline. In this blog post, we will focus on one such tracker, FairMOT, that revolutionised the joint optimisation of detection and re-identification tasks in tracking. The metrics that was calculated in our DeepSort post did not show good results either. The average accuracy that we got was 28.6, which is very low. FairMOT was introduced to tackle the re-identification problem.