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Based on the observation that motion of different pixels from the same target has very similar spatial-temporal properties in bus video surveillance images, a feature point's trajectory clustering method is proposed to estimate passenger flow in this paper. Firstly, the pyramid-based optical flow algorithm is utilized to tracking the feature point's movement in the images; then, their trajectories...
This paper proposes a new approach to describe traffic scene including vehicle collisions and vehicle anomalies at intersections by video processing and motion statistic techniques. The research mainly targets on extracting abnormal event characteristics at intersections and learning normal traffic flow by trajectory clustering techniques. Detecting and analyzing accident events are done by observing...
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...
In this work, we propose a method for tracking individuals in crowds. Our method is based on a trajectory-based clustering approach that groups trajectories of image features that belong to the same person. The key novelty of our method is to make use of a person's individuality, that is, the gait features and the temporal consistency of local appearance to track each individual in a crowd. Gait features...
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