Lightweight Anomaly Detection Mechanism Based on Machine Learning Using Low-Cost Surveillance Cameras

Author:

Lee Yeon-JiORCID,Park Na-EunORCID,Lee Il-GuORCID

Publisher

Springer Nature Singapore

Reference14 articles.

1. Soheil, V., Kin-Choong, Y.: A CNN-RNN combined structure for real-world violence detection in surveillance cameras. Appl. Sci. 12(3) (2022)

2. Trong, N., Jean, M.: Anomaly detection in video sequence with appearance-motion correspondence. In: 2019 IEEE/CVF International Conference on Computer Vision, ICCV, Seoul Korea (2019)

3. Asif, K., Quadri S.M.K., Saba, B., Junaid, L.: Deep diagnosis: a real-time apple leaf disease detection system based on deep learning. Comput. Electron. Agric. 298 (2022)

4. Amreen, A., Sweta, J., Mahesh, G., Swetha, V.: Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput. Electron. Agric. 187 (2021)

5. Waqas, A., Umair, N., Muhammad, H.Y., Sergio, A.V.: Lightweight CNN and GRU network for real-time action recognition. In: 2022 12th International Conference on Pattern Recognition Systems, ICPRS, Saint-Etienne, France (2022)

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