Motion pattern analysis in crowded scenes by using density based clustering

Video surveillance is always a hot topic in computer vision. With the public safe issue received more and more attention, analysis for crowd motion is becoming significant, and detecting motion patterns or activities in crowded scenes from videos is one of the major problem in crowd analysis. This paper proposes a new method for learning the motion patterns in crowded scenes. We add the direction information to the motion vectors, and cluster the data by a density based clustering. We extract the feature points using KLT corner extractor and track them to obtain basic motion information by optical flow techniques. All the motion information in different frames forms the motion flow field. Improved DBSCAN method is used to divide the motion flow filed into different patterns. The result of the system is given as a graph with groups of vectors. The experiment result in real-world videos is presented to demonstrate our approach.