Automatic Topic Discovery for Multi-Object Tracking


  • Wenhan Luo Imperial College London
  • Björn Stenger Toshiba Research Europe
  • Xiaowei Zhao Imperial College London
  • Tae-Kyun Kim Imperial College London



topic model, multi-object tracking


This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet Process Mixture Model (DPMM). The tracking problem is cast as a topic-discovery task where the video sequence is treated analogously to a document. This formulation addresses tracking issues such as object exclusivity constraints as well as cannot-link constraints which are integrated without the need for heuristic thresholds. The video is temporally segmented into epochs to model the dynamics of word (superpixel) co-occurrences and to model the temporal damping effect. In experiments on public data sets we demonstrate the effectiveness of the proposed algorithm.




How to Cite

Luo, W., Stenger, B., Zhao, X., & Kim, T.-K. (2015). Automatic Topic Discovery for Multi-Object Tracking. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).