Abstract
In this paper, a novel approach for automatic anomaly detection in surveillance video is proposed. It is highly efficient for real-time detection. It can also handle multi-scale detection and can cope with both spatial and temporal anomalies. Specifically, features capturing both appearance and motion characteristic are extracted from densely sampled spatio-temporal video volume (STV). And to bridge the semantic gap between low-level visual feature and high-level event, we use the middle-level visual attributes as the intermediary. These three-level framework is modeled as an Extreme Learning Machine (ELM) which can effectively and efficiently tell whether a STV belongs to an anomalous event. We also use the Spatio-temporal Pyramid (STP) to capture the spatial and temporal continuity of an anomalous event , enabling our approach to cope with multi-scale and complicated events. Experiments on several datasets are carried out and the superior performance compared to state-of-the-art approaches verifies the effectiveness of our approach.
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Xiao, T., Zhang, C., Zha, H., Wei, F. (2015). Anomaly Detection with ELM-Based Visual Attribute and Spatio-temporal Pyramid. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, KA. (eds) Proceedings of ELM-2014 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-14066-7_30
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DOI: https://doi.org/10.1007/978-3-319-14066-7_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14065-0
Online ISBN: 978-3-319-14066-7
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