Enhancing network flow for multi-target tracking with detection group analysis

Multi-target tracking (MTT) has been a research hotspot in the field of computer vision. The objective is forming the trajectory of multiple targets in a given video. However, the useful detection and tracklet relationship during the tracking process are not fully explored in most current algorithms and it leads to the accumulation of errors. We introduce a novel Detection Group, which includes the detections within a temporal and spatial threshold and then model the relationship between Detection Group(DG) and close tracklets. Although the minimum-cost network flow algorithm has been proven to be a successful strategy for multi-target tracking, but it still has one main drawback: due to the fact that useful corresponding detection and tracklet relationships are not well modeled, the network flow based tracker can only model low-level detection relationship without high-level detection set information. To cope with this problem, we extend the classical minimum-cost network flow algorithm within the tracking-by-detection paradigm by incorporating additional constraints. In our experiment, we achieved encouraging result on the MOT17 benchmark and our result is comparable to the current state of the art trackers.