PhD Forum: Investigating the performance of a multi-modal approach to unusual event detection
Kuklyte, Jogile and Kelly, Philip and O'Connor, Noel E. (2011) PhD Forum: Investigating the performance of a multi-modal approach to unusual event detection. In: ACM/IEEE International Conference on Distributed Smart Cameras, 22-25 Aug 2011, Ghent, Belgium. ISBN 978-1-4577-1706-2
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In this paper, we investigate the parameters under- pinning our previously presented system for detecting unusual events in surveillance applications . The system identifies anomalous events using an unsupervised data-driven approach. During a training period, typical activities within a surveilled environment are modeled using multi-modal sensor readings. Significant deviations from the established model of regular activity can then be flagged as anomalous at run-time. Using this approach, the system can be deployed and automatically adapt for use in any environment without any manual adjustment. Experiments carried out on two days of audio-visual data were performed and evaluated using a manually annotated ground- truth. We investigate sensor fusion and quantitatively evaluate the performance gains over single modality models. We also investigate different formulations of our cluster-based model of usual scenes as well as the impact of dynamic thresholding on identifying anomalous events. Experimental results are promis- ing, even when modeling is performed using very simple audio and visual features.
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