Detecting Dominant Motion Flows in Unstructured/Structured Crowd Scenes

Detecting dominant motion flows in crowd scenes is one of the major problems in video surveillance. This is particularly difficult in unstructured crowd scenes, where the participants move randomly in various directions. This paper presents a novel method which utilizes SIFT features' flow vectors to calculate the dominant motion flows in both unstructured and structured crowd scenes. SIFT features can represent the characteristic parts of objects, allowing robust tracking under non-rigid motion. First, flow vectors of SIFT features are calculated at certain intervals to form a motion flow map of the video. Next, this map is divided into equally sized square regions and in each region dominant motion flows are estimated by clustering the flow vectors. Then, local dominant motion flows are combined to obtain the global dominant motion flows. Experimental results demonstrate the successful application of the proposed method to challenging real-world scenes.