Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Unsupervised Motion Learning for Abnormal Behavior Detection in Visual Surveillance

영상감시시스템에서 움직임의 비교사학습을 통한 비정상행동탐지

  • 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)
  • 정하욱 (서울대학교 공과대학 전기.컴퓨터 공학부) ;
  • 장형진 (서울대학교 공과대학 전기.컴퓨터 공학부) ;
  • 최진영 (서울대학교 공과대학 전기.컴퓨터 공학부)
  • Received : 2011.04.22
  • Accepted : 2011.08.10
  • Published : 2011.09.25
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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.

본 논문에서는 비교사학습법을 통해 영상의 방대한 정보를 효율적으로 모델링 하는 방법을 제안하고자 한다. 여기서 이동궤적들은 자연어 처리에 사용되는 알고리즘인 잠재 디리클레 할당 모형(Latent Dirichlet Allocation)에 의해 직진, 좌회전, 우회전등 각 상황 별로 주제에 따라 그 영역을 효과적으로 분류할 수 있다. LDA를 이용해 주제별로 의미 있는 영역을 분류한 후, 각 주제별로 분류된 궤적을 관측열로 보고 은닉 마르코프 모델(Hidden Markov Model)의 바움-웰치 알고리즘을 사용하여 학습한다. 전향 알고리즘을 사용하여 입력된 행동과 학습된 행동을 비교함으로써 영상내의 행동이 정상인지 비정상인지를 효과적으로 판단할 수 있다. 실험결과 다양한 영상에 대해 의미있는 주제별로 영역이 잘 분류되며 추적에러로 인한 궤적의 노이즈에도 강인하게 물체의 무단횡단, 신호위반과 같은 상황을 효과적으로 탐지하는 것을 확인할 수 있다.

Keywords

References

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