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In this paper we introduce a novel method to detect and localize abnormal behaviors in crowd videos using Social Force model. For this purpose, a grid of particles is placed over the image and it is advected with the space-time average of optical flow. By treating the moving particles as individuals, their interaction forces are estimated using social force model. The interaction force is then mapped...
We propose a 2D model-based approach for tracking human body parts during articulated motion. A human is modeled as a stick figure with thirteen landmarks, and an action is a sequence of these stick figures. Given the locations of these joints in a model video and only the first frame of a test video, the joint locations are automatically estimated throughout the test video using two geometric constraints...
In this paper we present a novel approach using a 4D (x,y,z,t) action feature model (4D-AFM) for recognizing actions from arbitrary views. The 4D-AFM elegantly encodes shape and motion of actors observed from multiple views. The modeling process starts with reconstructing 3D visual hulls of actors at each time instant. Spatiotemporal action features are then computed in each view by analyzing the...
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