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 language | English |
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Pages (from-to) | 415-426 |
Number of pages | 12 |
Journal | Pattern Analysis and Applications |
Volume | 15 |
Issue number | 4 |
DOIs | |
Publication status | Published - Nov 2012 |
Externally published | Yes |
Keywords
- Hidden Markov model
- Probability product kernel
- Traffic monitoring
- Trajectory clustering
- Video surveillance