Abstract
Nowadays, the analysis of abnormal events becomes more and more exhausting due to the divine use of surveillance cameras. This paper proposes a novel approach to predict and localize anomaly events. In this paper, a new framework for motion extraction called BQM is proposed. Then, the regions of interest are extracted and a filtering process is applied to eliminate the non-significant ones. However, for more precision, the HFG descriptor is applied for each region already divided into non-overlapping cells, Finally, we have evaluated our method using UCSD and Avenues datasets. The Sparse Auto-encoder, an instance of a deep learning strategy is presented for efficient abnormal activity detection and the Softmax for the classification.
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The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.
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Gnouma, M., Ejbali, R., Zaied, M. (2020). Video Anomaly Detection and Localization in Crowded Scenes. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_9
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