  In existing joint detection and tracking methods, pairwise relational
features are used to match previous tracklets to current detections. However,
the features may not be discriminative enough for a tracker to identify a
target from a large number of detections. Selecting only high-scored detections
for tracking may lead to missed detections whose confidence score is low.
Consequently, in the online setting, this results in disconnections of
tracklets which cannot be recovered. In this regard, we present Sparse Graph
Tracker (SGT), a novel online graph tracker using higher-order relational
features which are more discriminative by aggregating the features of
neighboring detections and their relations. SGT converts video data into a
graph where detections, their connections, and the relational features of two
connected nodes are represented by nodes, edges, and edge features,
respectively. The strong edge features allow SGT to track targets with tracking
candidates selected by top-K scored detections with large K. As a result, even
low-scored detections can be tracked, and the missed detections are also
recovered. The robustness of K value is shown through the extensive
experiments. In the MOT16/17/20 and HiEve Challenge, SGT outperforms the
state-of-the-art trackers with real-time inference speed. Especially, a large
improvement in MOTA is shown in the MOT20 and HiEve Challenge. Code is
available at https://github.com/HYUNJS/SGT.
