Learning Non-Uniform Hypergraph for Multi-Object Tracking


  • Longyin Wen JD Finance
  • Dawei Du University at Albany, SUNY
  • Shengkun Li University at Albany, SUNY
  • Xiao Bian GE Global Research
  • Siwei Lyu University at Albany, SUNY




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.




How to Cite

Wen, L., Du, D., Li, S., Bian, X., & Lyu, S. (2019). Learning Non-Uniform Hypergraph for Multi-Object Tracking. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 8981-8988. https://doi.org/10.1609/aaai.v33i01.33018981



AAAI Technical Track: Vision