On Detection, Data Association And Segmentation For Multi-Target Tracking

In this work, we propose a tracker that differs from most existing multi-target trackers in two major ways. Firstly, our tracker does not rely on a pre-trained object detector to get the initial object hypotheses. Secondly, our tracker\u27s final output is the fine contours of the targets rather than traditional bounding boxes. Therefore, our tracker simultaneously solves three main problems: detection, data association and segmentation. This is especially important because the output of each of those three problems are highly correlated and the solution of one can greatly help improve the others. The proposed algorithm consists of two main components: structured learning and Lagrange dual decomposition. Our structured learning based tracker learns a model for each target and infers the best locations of all targets simultaneously in a video clip. The inference of our structured learning is achieved through a new Target Identity-aware Network Flow (TINF). The second component is Lagrange dual decomposition, which combines the structured learning tracker with a multi-label Conditional Random Field (CRF) based segmentation algorithm. This leads to more accurate segmentation results and also helps better resolve typical difficulties in multiple target tracking, such as occlusion handling, ID-switch and track drifting