Trajectory clustering in CCTV traffic videos using probability product kernels with hidden Markov models

Research output: Indexed journal article Articlepeer-review

12 Citations (Scopus)

Abstract

This article presents a method for clustering the trajectories obtained by tracking vehicles in traffic videos, recorded from CCTV cameras in public spaces. The proposed method employs a model-based approach in which (1) each trajectory (position and velocity) is modelled using a hidden Markov model (HMM), and (2) the distance between two trajectories is computed as the probabilistic similarity between HMMs, by means of the probability product kernel. Experiments on a set of real traffic video sequences reveal very good results of the proposed approach, outperforming two non-trivial baselines. To the best of the authors' knowledge, this approach is novel for trajectory grouping in traffic videos.

Original languageEnglish
Pages (from-to)415-426
Number of pages12
JournalPattern Analysis and Applications
Volume15
Issue number4
DOIs
Publication statusPublished - Nov 2012
Externally publishedYes

Keywords

  • Hidden Markov model
  • Probability product kernel
  • Traffic monitoring
  • Trajectory clustering
  • Video surveillance

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