Abstract
In this chapter, we introduce a method for trajectory pattern analysis through the probabilistic inference model with both regional and velocity observations. By embedding Gaussian models into the discrete topic model framework, our method uses continuous velocity as well as regional observations unlike the existing approaches. In addition, the proposed framework combined with Hidden Markov Model can cover the temporal transition of the scene state, which is useful in checking violation of the rule that some conflict topics (e.g., two cross traffic patterns) should not occur at the same time. To achieve online learning even with the complexity of the proposed model, we suggest a novel learning scheme instead of collapsed Gibbs sampling. The proposed two-stage greedy learning scheme is not only efficient at reducing the search space but also accurate in a way that the accuracy of online learning becomes not worse than that of the batch learning. To validate the performance of our method, experiments were conducted on various datasets. Experimental results show that our model explains satisfactorily the trajectory patterns with respect to scene understanding, anomaly detection, and prediction.
This chapter is the reduced version of the authors paper [12] with Copyright ©Springer-Verlag Berlin Heidelberg, 2014.
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Notes
- 1.
To concisely represent notations, the set notation \( \{ \cdot \} \) without the range of index is defined as a set of variables containing all possible indices. Also, the variables without indices imply that they deal with all possible indices, such as,
\( c = \left\{ {c_{tji} } \right\} = \left\{ {c_{tji} } \right\}_{t = 1,j = 1,i = 1}^{{T,M,N_{j} }} ,p(s) = p\left( {\{ s_{t} \}_{t = 1}^{T} } \right) = \prod\limits_{t = 1}^{T} p(s_{t} ). \).
- 2.
Because the anomaly detection task should be performed for every frame, we compose \( t^{\prime} \)-th trajectory collections from the trajectories on the current frame.
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Acknowledgments
This work was sponsored by Samsung Techwin Co.,Ltd and BK 21 plus program, and also partially supported by the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as the Global Frontier Project.
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Jeong, H., Yoo, Y., Yi, K.M., Choi, J.Y. (2016). Traffic Pattern Analysis and Anomaly Detection via Probabilistic Inference Model. In: Kyung, CM. (eds) Theory and Applications of Smart Cameras. KAIST Research Series. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9987-4_10
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