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On-line abnormality detection in video without the use of object detection and tracking is a desirable task in surveillance.We address this problem for the case when labeled information about normal events is limited and information about abnormal events is not available. We formulate this problem as a one-class classification, where multiple local novelty classifiers (detectors) are used to first...
This paper starts from the idea of automatically choosing the appropriate thresholds for a shadow detection algorithm. It is based on the maximization of the agreement between two independent shadow detectors without training data. Firstly, this shadow detection algorithm is described and then, it is adapted to analyze video surveillance sequences. Some modifications are introduced to increase its...
In this paper we propose a real-time algorithm for detecting and tracking moving objects in a video sequence. Based on the on-line boosting framework, our algorithm is able to detect an object as a member of a class, e.g. pedestrian, then a specific model for each instance of the class can be built on-line allowing at the same time robust tracking and recognition of the particular instance as it leaves...
Which cues to be used in describing pedestrian are the key to detect pedestrian with Adaboost algorithm. In this paper we presented 4 triangle cues and 16 complex cues by researching on pedestrianpsilas figures and proposed the way to calculate these cuespsila sum. To evaluate this calculating method, we proved the relation of iterative times with error and time spending. Compared with rectangle and...
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