Detection of dominant flow and abnormal events in surveillance video

作者：Kwak Sooyeong^*; Byun Hyeran

来源：Optical Engineering, 2011, 50(2): 027202.

DOI：10.1117/1.3542038

• abnormal event detection
• dominant flows
• abnormally moving objects
• abnormal direction
• abnormal speeds

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

• 出版日期2011-2