Low-Cost CNN for Automatic Violence Recognition on Embedded System

IEEE Access

Joelton Cezar Vieira¹  Valderi Reis Quietinho Leithardt¹  Stefano Frizzo Stefenon¹  Gabriel Schneider de Jesus²  Fabio Luis Perez³  Andreza Sartori³ 

[1] Department of Telecom., Electrical and Mechanical Engineering, Regional University of Blumenau (FURB). Rua S&x00E3;Fondazione Bruno Kessler. Via Sommarive 18, Trento, Povo, Italy;o Paulo 3250, Blumenau, Brazil;

关键词: Neural networks;    artificial neural networks;    image processing;    image classification;   

DOI  :  10.1109/ACCESS.2022.3155123

来源: DOAJ

【 摘 要 】

Due to the increasing number of violence cases, there is a high demand for efficient monitoring systems, however, these systems can be susceptible to failure. Therefore, this work proposes the analysis and application of low-cost Convolutional Neural Networks (CNNs) techniques to automatically recognize and classify suspicious events. Thus, it is possible to alert and assist the monitoring process with a reduced deployment cost. For this purpose, a dataset with violence and non-violence actions in scenes of crowded and non-crowded environments was assembled. The mobile CNNs architectures were adapted and obtained a classification accuracy of up to 92.05%, with a low number of parameters. To demonstrate the models’ validity, a prototype was developed by using an embedded Raspberry Pi platform, able to execute a model in real-time with 4 frames-per-second of speed. In addition, a warning system was developed to recognize pre-fight behavior and anticipate violent acts, alerting security to potential situations.

【 授权许可】

Unknown