Use of feedback strategies in the detection of events for video surveillance
Video Process. & Understanding Lab., Univ. Autonoma de Madrid, Madrid, SpainIET Computer Vision (Impact Factor: 0.96). 10/2011; 5(5):309 - 319. DOI: 10.1049/iet-cvi.2010.0047
Source: IEEE Xplore
The authors present a feedback-based approach for event detection in video surveillance that improves the detection accuracy and dynamically adapts the computational effort depending on the complexity of the analysed data. A core feedback structure is proposed based on defining different levels of detail for the analysis performed and estimating the complexity of the data being analysed. Then, three feedback-based analysis strategies are defined (based on this core structure) and introduced in the processing stages of a typical video surveillance system. A rule-based system is designed to manage the interaction between these feedback-strategies. Experimental results show that the proposed approach slightly increases the detection reliability, whereas highly reduces the computational effort as compared to the initially developed surveillance system (without feedback strategies) across a variety of multiple video surveillance scenarios operating at real time.
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ABSTRACT: Video surveillance systems in public areas are grown rapidly for safety and security; therefore, the number of monitors becomes too large to watch by human. Automatic event detection system becomes more important. A trouble of surveillance camera in pedestrianly areas is that position of camera is too far or too close to the target objects and it compromises detection performance. In order to limit effects of camera positions, this paper proposes an event detection framework using grid-based features, which is a combination of localized information and event rules. Relationship between grid resolution and accuracy performance of event detection is studied. Grid-based features are tested on Neural Network and SVM classifiers. Experimental results show that grid-based features perform better than non-grid features. Performance of learning machines is also related to event types and grid size. The larger grid size is appropriate for the farther camera position.
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