Abstract
As the importance of surveillance in the world increases, the importance of surveillance cameras has increased. It has also increased the number of surveillance cameras installed. On the other hand, as the number of surveillance cameras increases, detecting abnormal events through monitoring requires higher labor intensity. The need for machine resources to detect abnormal events in stored videos has been expanded. Simultaneously, due to the rapid development of the computer vision field, a method has been developed in which the machine can detect abnormal events. To detect abnormal events, the importance of visual features that the machine can recognize has also increased. Previous methods used deep-learned visual features to detect anomalies, while feature extraction of visual features uses only a single stride and a single segment for the time scale. That is, abnormal events are detected by considering only a single scale. These features are spatiotemporal, but there is a possibility that the accuracy can be increased through various strides and segments. Therefore, we propose to consider various strides and various segments, i.e., multi-time scales. We evaluate performance with small datasets reconstructed from existing datasets.
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References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105, (NIPS 2012)
Tran, D., et al.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489–4497, (ICCV 2015)
Sun, D., et al.: Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8934–8943, (CVPR 2018)
Sultani, W., Chen, C., Shah, M.: Real-world anomaly detection in surveillance videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6479–6488, (CVPR 2018)
Zhu, Y., Newsam, S.: Motion-aware feature for improved video anomaly detection. In: Proceedings of the British Machine Vision Conference, (BMVC 2019)
Acknowledgements
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.2016-0-00406, SIAT CCTV Cloud Platform).
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Gim, UJ., Kim, JH., Park, YH., Nasridinov, A. (2021). Multi-time Scale Features for Anomaly Detection from Surveillance Videos. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 212. Springer, Singapore. https://doi.org/10.1007/978-981-33-6757-9_37
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DOI: https://doi.org/10.1007/978-981-33-6757-9_37
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