Object Detection and Movement Patterns Using Neural Networks and Genetic Algorithms for the Identification of Armed Robbery
Abstract
In Latin America there are 42 of the 50 most violent cities in the world, in Peru in 2019 9.7% of criminal acts with firearms were carried out in urban areas and in cities with 20,000 or more inhabitants the percentage rose to 10.2%, but the complaints, due to lack of evidence, generated in cities like Lima, only 19.46% of arrests. The members of the police force have devices, vehicles and tools that allow them to carry out their functions in a safe manner, however, they do not have an effective mechanism, which allows to identify an armed robbery and to concentrate their efforts on carrying out a timely intervention. Therefore, the present research develops an algorithm based on Recurrent Neural Networks with OpenCv / YOLOv3 combined with the Genetic Algorithms technique for the detection of objects and movement patterns with 96.5% accuracy, allowing early detection of a crime perpetrated under the modality of armed robbery.
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