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Recognition of violent actions on streets in urban spaces using Machine Learning in the context of the Covid-19 pandemic
IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET) ; : 2201-2204, 2021.
Article in English | Web of Science | ID: covidwho-1927524
ABSTRACT
Currently, recognition systems based on Artificial Intelligence and Computer Vision have enabled various applications in fields such as Medicine, Industrial Engineering, and in an emerging way in the field of Public Safety as a useful and necessary tool in smart cities that favours the control, management and prevention of criminal acts. Given that violence is a very frequent social problem in Latin American countries. A pilot case has been proposed in the city of Iquitos, Peru, with a tool generated to recognise violent actions from a video or image captured from a mobile phone. This work proposes the application of a mobile tool that facilitates the recognition of high-frequency violent actions on public roads. A bank of 500 images has been generated for each class of violent action prioritised in this work, then a manual labelling tool called "LabelImg" has been used with the extraction of FPS from videos, and the convolutional neural network algorithm YOLO v3 has been used with the Darknet variant. The results of the experiment achieved an accuracy of 94% in the detection of 4 violent actions punching, kicking, grappling and strangling.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET) Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: IEEE International Conference on Electrical, Computer, and Energy Technologies (ICECET) Year: 2021 Document Type: Article