Addressing Ambiguity in Multi-target Tracking by Hierarchical Strategy
This work addresses ambiguity in multi-target tracking for video analysis, presenting an incremental improvement over existing methods.
The paper tackles the problem of multi-target tracking in video by introducing a hierarchical approach that uses a network flow method and a new scoring system called ConfRank to link detections and tracklets, resulting in competitive performance with lower identity switches on several datasets.
This paper presents a novel hierarchical approach for the simultaneous tracking of multiple targets in a video. We use a network flow approach to link detections in low-level and tracklets in high-level. At each step of the hierarchy, the confidence of candidates is measured by using a new scoring system, ConfRank, that considers the quality and the quantity of its neighborhood. The output of the first stage is a collection of safe tracklets and unlinked high-confidence detections. For each individual detection, we determine if it belongs to an existing or is a new tracklet. We show the effect of our framework to recover missed detections and reduce switch identity. The proposed tracker is referred to as TVOD for multi-target tracking using the visual tracker and generic object detector. We achieve competitive results with lower identity switches on several datasets comparing to state-of-the-art.