Detection of Acrimonious Attacks using Deep Learning Techniques and Edge Computing Devices

We proposed a novel approach for an automatic attack alerting system that is helpful for citizens. Our proposed model uses the YOLO object detection model, which can detect violent objects and the pose estimation model, which can estimate dangerous actions. Then, the output of these two models is entered into the LSTM model as input at the same time and the LSTM model is used to classify the type of attacks. The whole system is deployed into edge devices. Finally, based on the output from the LSTM model. If the threat is detected, the edge device can notify an automatic alert to the victim’s family members or friends to minimize harm. The performance proves that the proposed model can detect the objects and accurately classify the attack types.