A Framework for Real-Time Crowd Behaviour Monitoring System Based on Human Activity Recognition in Surveillance Videos

Author:

Srinivasa Perumal R.,Janarthanan P. M.,Keerthiharan T.,Lakshmi Priya G. G.

Publisher

Springer Nature Singapore

Reference20 articles.

1. Hodgetts HM et al. (2017) See no evil: cognitive challenges of security surveillance and monitoring. J Appl Res Memory and Cogni 6.3:230–243

2. Ha, H, Xin L, Mengting L(2019) A detection method of abnormal event in crowds based on image entropy. In: Proceedings of the 4th international conference on intelligent information processing

3. Steiger C, Walder H, Platzner M (2004) Operating systems for reconfigurable embedded platforms: online scheduling of realtime tasks. IEEE Trans Comput 53(11):1393–1407

4. Aldissi B, Ammar H (2020) Real-time frequency-based detection of a panic behavior in human crowds. Multimed Tools Appl 79(33):24851–24871

5. Kh R, Ghezelbash MR, Haddadnia J, Delbari A (2012) An intelligent surveillance system for falling elderly detection based on video sequences. In: 19th Iranian conference of biomedical engineering (ICBME), Tehran, Iran, December, pp 20–21

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