Detecting Violent Behaviour on Edge Using Convolutional Neural Networks

A new portable solution is proposed based on Convolutional Neural Networks (CNN) to increase the speed and accuracy of detecting violence behaviour on edge devices. This solution has numerous applications in public safety. A combination of surveillance using CCTV cameras and Unmanned Aerial Vehicles...

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Bibliographic Details
Main Authors: Golcarenarenji, Gelayol, Khusainov, Rinat, Gegov, Alexander, Martinez-Alpiste, Ignacio
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Cameras
CCTV Cameras
Convolutional neural networks
Edge Computing
Image edge detection
Intelligent systems
Public security
Real-time systems
Surveillance
Throughput
UAVs
Violent Behaviour
Visualization
YOLOV5
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Description
Summary:A new portable solution is proposed based on Convolutional Neural Networks (CNN) to increase the speed and accuracy of detecting violence behaviour on edge devices. This solution has numerous applications in public safety. A combination of surveillance using CCTV cameras and Unmanned Aerial Vehicles (UAVs) is used to demonstrate the real-world surveillance use cases to monitor abnormal behaviors in public. The proposed solution delivers 95.01% accuracy while taking 13.2ms for inference on GeForce GTX 1660 Ti GPU and reaching 38 frames per second throughput on Jetson AGX Orin measured on a combination of Drone-action and chu-surveillance-violence-detection datasets. The results show the strong practical application potential of the proposed solution in terms of real-time performance, visual quality, and high accuracy.
ISSN:2767-9802
DOI:10.1109/IS61756.2024.10705272