  The majority of Multi-Object Tracking (MOT) algorithms based on the
tracking-by-detection scheme do not use higher order dependencies among objects
or tracklets, which makes them less effective in handling complex scenarios. In
this work, we present a new near-online MOT algorithm based on non-uniform
hypergraph, which can model different degrees of dependencies among tracklets
in a unified objective. The nodes in the hypergraph correspond to the tracklets
and the hyperedges with different degrees encode various kinds of dependencies
among them. Specifically, instead of setting the weights of hyperedges with
different degrees empirically, they are learned automatically using the
structural support vector machine algorithm (SSVM). Several experiments are
carried out on various challenging datasets (i.e., PETS09, ParkingLot sequence,
SubwayFace, and MOT16 benchmark), to demonstrate that our method achieves
favorable performance against the state-of-the-art MOT methods.

Computer Vision and Pattern Recognition