Abstract
Anomalous event detection in any surveillance system has become an important area of research to make the surveillance effective and real time. In recent years, deep learning schemes are predominant to improve the detection accuracy. However, due to high computational complexity associated in deep learning architectures, it becomes a challenge to implement them in real-time scenarios. In this paper we propose a scheme to detect anomalous event in real time surveillance video. A database pre-processing algorithm has been proposed to capture the spatial and temporal frames in every second, which is subsequently utilized in two-stream 2D-CNN architecture for feature extraction and classification. A standard dataset, UCF-crime has been used to validate the proposed method. Finally, a comparative analysis has been made and it is observed that the classification accuracy and area under curve (AUC) of the suggested scheme is superior as compared to the recently proposed competent schemes.
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Majhi, S., Dash, R., Sa, P.K. (2020). Two-Stream CNN Architecture for Anomalous Event Detection in Real World Scenarios. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_31
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DOI: https://doi.org/10.1007/978-981-15-4018-9_31
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