Abstract
The real-time events are fast and occurring at highly dynamical moments. Hence, the important challenges are identifying the anomaly incidents properly. The specified methods and techniques are to be quick in identification for control and other measures of the events. In the proposed method, the anomalies are detected from the surveillance videos using the multiple instance learning and ID3 for extracting the features. The extracted features are then used as input to a deep neural network where the classification of the videos to anomalous and normal videos is done. The investigated dataset is with 128 hours of videos with ten percent of different realistic anomaly videos. The AUC of the proposed approach is 81. The proposed approach is most beneficial for the real-world anomaly recognition in surveillance videos.
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Cherian, A.K., Poovammal, E. (2021). Anomaly Detection in Real-Time Surveillance Videos Using Deep Learning. In: Smys, S., Tavares, J.M.R.S., Bestak, R., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1318. Springer, Singapore. https://doi.org/10.1007/978-981-33-6862-0_19
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