Low-Cost CNN for Automatic Violence Recognition on Embedded System

Vieira, J. C.; Sartori, Andreza; Stefenon, Stéfano Frizzo; Perez, Fabio Luis; Schneider De Jesus, Gabriel; LEITHARDT, VALDERI

http://hdl.handle.net/10400.26/43566

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Autores

Vieira, J. C.

Sartori, Andreza

Stefenon, Stéfano Frizzo

Perez, Fabio Luis

Schneider De Jesus, Gabriel

LEITHARDT, VALDERI

Resumo(s)

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.