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Unsupervised Motion Learning for Abnormal Behavior Detection in Visual Surveillance  

Jeong, Ha-Wook (Electrical Engineering and Computer Science, Engineering College, Seoul National University)
Chang, Hyung-Jin (Electrical Engineering and Computer Science, Engineering College, Seoul National University)
Choi, Jin-Young (Electrical Engineering and Computer Science, Engineering College, Seoul National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SC / v.48, no.5, 2011 , pp. 45-51 More about this Journal
Abstract
In this paper, we propose an unsupervised learning method for modeling motion trajectory patterns effectively. In our approach, observations of an object on a trajectory are treated as words in a document for latent dirichlet allocation algorithm which is used for clustering words on the topic in natural language process. This allows clustering topics (e.g. go straight, turn left, turn right) effectively in complex scenes, such as crossroads. After this procedure, we learn patterns of word sequences in each cluster using Baum-Welch algorithm used to find the unknown parameters in a hidden markov model. Evaluation of abnormality can be done using forward algorithm by comparing learned sequence and input sequence. Results of experiments show that modeling of semantic region is robust against noise in various scene.
Keywords
비정상행동탐지;이동궤적 모델링;잠재 디리클레 할당 모형;은닉 마르코프 모델;영상감시;
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