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In this paper, we propose a macro-observation scheme for unusual event detection in daily life, where motions in time-space domain are described by a global representation and individual activities do not have to be defined and modeled beforehand. The proposed representation records the time-space energy of motions of all moving objects in a scene without segmenting individual object parts or tracking...
We present an approach for unusual event detection, based on a tree of trackers. At lower levels, the trackers are trained on broad classes of targets. At higher levels, they aim at more specific targets. For instance, at the root, a general blob tracker could operate which may track any object. The next level could already use information about human appearance to better track people. A further level...
In this paper, we present an automatic classification framework combining appearance based features and hidden Markov models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class...
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