Robust multi-object tracking using confident detections and safe tracklets

This paper presents a novel approach to simultaneous tracking of multiple targets in a video. Instead of using the unreliable “detector confidence scores,” it develops a new scoring system, ConfRank, that originates from the PageRank idea where not only the detection confidence score, but that the quality and the quantity of adjacent detections in spatio-temporal neighborhood are considered. The new scoring system effectively separates False Positives from True Positives, that enables us to remove unwanted detections using a simple threshold without loosing targets. Our framework outperforms state-of-the-art tracking methods in most evaluations. Specifically, it significantly reduces False Positives and switch identities while keeping missed detections low leading to higher precision and multiple object tracking accuracy (MOTA) on several standard datasets.