Author:
Garg Aishvarya,Nigam Swati,Singh Rajiv
Publisher
Springer Nature Singapore
Reference52 articles.
1. Franklin, R. J., & Dabbagol, V. (2020). Anomaly detection in videos for video surveillance applications using neural networks. In 2020 Fourth International Conference on Inventive Systems and Controls (ICISC) (p. 632). IEEE.
2. Sellat, H. (2019). Anomaly detection in videos using LSTM convolutional autoencoder. https://towardsdatascience.com/prototyping-an-anomaly-detection-system-for-videos-step-by-step-using-lstm-convolutional-4e06b7dcdd29. Last accessed 25 April.
3. Garg, A., Nigam, S., & Singh, R. (2022). Vision based human activity recognition using hybrid deep learning. In 2022 International Conference on Connected Systems & Intelligence (CSI) (pp. 1–6). IEEE.
4. Dhiman, C., & Vishwakarma, D. K. (2019). A review of state-of-the-art techniques for abnormal human activity recognition. Engineering Applications of Artificial Intelligence, 77, 21–45.
5. Sreenu, G., & Durai, S. (2019). Intelligent video surveillance: A review through deep learning techniques for crowd analysis. Journal of Big Data, 6(1), 1–27.