Real-time Violence Activity Detection Using Deep Neural Networks in a CCTV camera

Dhruv Shindhe, S and Govindraj, S and Omkar, SN (2021) Real-time Violence Activity Detection Using Deep Neural Networks in a CCTV camera. In: 7th IEEE International Conference on Electronics, Computing and Communication Technologies, 9-11 Jul 2021, Bangalore.

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Abstract

In surveillance systems one of the most difficult and critical tasks is detecting violence, most video surveillance systems are faced with the challenges of false alarms and working in a real-time environment. This paper introduces a lightweight system that can be employed in real-time systems. The system proposed uses OpenPose for multi-person 2D pose estimation, YoloV3 for person detection, and a CNN to classify the violent action. OpenPose unlike other systems uses a bottom-up approach which decouples the running time from the number of people in the frame, YoloV3 deployed for person detection has a very fast processing time and the CNN used was trained on the skeleton dataset that was generated and had very good accuracy. We also propose an skeleton image dataset of three action categories, namely kicking, punching, and non-violent. © 2021 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of CONECCT 2021: 7th IEEE International Conference on Electronics, Computing and Communication Technologies
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Deep neural networks; Interactive computer systems; Musculoskeletal system; Real time systems, Action recognition; Activity detection; CNN; Critical tasks; Openpose; Person detection; Real- time; Surveillance systems; Video surveillance systems; Yolov3, Security systems
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	07 Feb 2022 12:18
Last Modified:	07 Feb 2022 12:18
URI:	http://eprints.iisc.ac.in/id/eprint/71275

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