Learning Non-Uniform Hypergraph for Multi-Object Tracking
This work addresses limitations in multi-object tracking for complex scenarios, representing an incremental improvement by introducing learned hyperedge weights.
The paper tackles the problem of multi-object tracking by modeling higher-order dependencies among tracklets using a non-uniform hypergraph, with experiments showing favorable performance against state-of-the-art methods on challenging datasets like MOT16.
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.