Abstract
In this paper, we present a method to detect and localize unusual activity in crowded scenes. A large number of surveillance cameras are fixed at various places for security purposes. We propose an autoencoder-based deep learning framework to categorize abnormality. Optical flow is computed using motion influence map and fed to the convolutional autoencoder. Thus, the spatio-temporal features obtained from the output of the encoder are used for classification. K-means clustering has been utilized to classify the spatio-temporal features. Experiments were conducted on standard crowd datasets and it is observed that the proposed model achieves comparable accuracy measure with state-of-the-art techniques.
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Agrawal, S., Dash, R. (2021). Anomaly Detection in Crowded Scenes Using Motion Influence Map and Convolutional Autoencoder. In: Chaki, N., Pejas, J., Devarakonda, N., Rao Kovvur, R.M. (eds) Proceedings of International Conference on Computational Intelligence and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-15-8767-2_14
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DOI: https://doi.org/10.1007/978-981-15-8767-2_14
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