Abstract
The present work addresses the task of identifying a predatory behavior of robbery of homes or businesses. The proposed objective is the detection of blunt elements used in the commission of the crime, limiting the context to barrettes, covered faces, people and gates (doors or windows). The proposal addresses the task of object identification applying Single Shot Detectors (SSD). Due to its versatility and the physical resources applied, the structure of SSD ResNet50 V1 FPN 640x640 has been chosen from the TensorFlow Model Zoo to train and validate the classification. This has been built in five classes, for the training and validation set, an average of 50 annotations per class have been processed. Additionally, a support function was worked on in the detection of human activity. The evaluated model obtained a mA of 69% in the detection of objects and in the identification of criminal behavior it showed a performance of 69%.
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