Abstract
In this paper we propose a deep learning framework to identify anomalous events in surveillance videos. Anomalous events are those which do not adhere to normal behaviour. We propose to use two discriminatively trained Convolutional Neural Networks, to capture the spatial and temporal features of videos, the classification scores obtained from the two streams are later fused to assign one final score. Since our approach is scenario-based, this eliminates the need for adopting a particular definition of anomaly. We show that the Two Stream CNNs perfectly capture the intricacies involved in modelling a video data by demonstrating the framework on airport and mall surveillance datasets respectively. We achieve a final test accuracy of 99.1% for spatial stream and 91% for temporal stream for airport scenario and an accuracy of 94.7% for spatial and 90.1% for the temporal stream for the mall scenario. Our framework can be easily implemented in real-time and is capable of detecting anomaly in each frame fed by a live surveillance system.
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Jamadandi, A., Kotturshettar, S., Mudenagudi, U. (2020). Two Stream Convolutional Neural Networks for Anomaly Detection in Surveillance Videos. In: Elçi, A., Sa, P., Modi, C., Olague, G., Sahoo, M., Bakshi, S. (eds) Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing, vol 766. Springer, Singapore. https://doi.org/10.1007/978-981-13-9683-0_5
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DOI: https://doi.org/10.1007/978-981-13-9683-0_5
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