Exploiting Hierarchical Dense Structures on Hypergraphs for Multi-Object Tracking

Longyin Wen, Zhen Lei, Siwei Lyu, Stan Z. Li, Ming Hsuan Yang

Research output: Contribution to journalArticle

34 Citations (Scopus)

Abstract

Most multi-object tracking algorithms are developed within the tracking-by-detection framework that consider the pairwise appearance similarities between detection responses or tracklets within a limited temporal window, and thus less effective in handling long-term occlusions or distinguishing spatially close targets with similar appearance in crowded scenes. In this work, we propose an algorithm that formulates the multi-object tracking task as one to exploit hierarchical dense structures on an undirected hypergraph constructed based on tracklet affinity. The dense structures indicate a group of vertices that are inter-connected with a set of hyperedges with high affinity values. The appearance and motion similarities among multiple tracklets across the spatio-temporal domain are considered globally by exploiting high-order similarities rather than pairwise ones, thereby facilitating distinguish spatially close targets with similar appearance. In addition, the hierarchical design of the optimization process helps the proposed tracking algorithm handle long-term occlusions robustly. Extensive experiments on various challenging datasets of both multi-pedestrian and multi-face tracking tasks, demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods.

Original languageEnglish
Article number7360186
Pages (from-to)1983-1996
Number of pages14
JournalIEEE transactions on pattern analysis and machine intelligence
Volume38
Issue number10
DOIs
Publication statusPublished - 2016 Oct 1

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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