Distributed Intelligent Video Surveillance for Early Armed Robbery Detection based on Deep Learning
This addresses the need for more reliable early detection of armed robberies in public spaces, particularly in high-crime regions like Latin America, though it is incremental as it builds on existing weapon detection methods.
The paper tackles the problem of false positives in weapon detection for armed robbery surveillance by proposing a distributed IoT system that combines weapon detection on end-devices with a cloud-based 3DCNN for scene classification, achieving a mAP of 0.87 for weapon detection and 0.88 accuracy for robbery detection.
Low employment rates in Latin America have contributed to a substantial rise in crime, prompting the emergence of new criminal tactics. For instance, "express robbery" has become a common crime committed by armed thieves, in which they drive motorcycles and assault people in public in a matter of seconds. Recent research has approached the problem by embedding weapon detectors in surveillance cameras; however, these systems are prone to false positives if no counterpart confirms the event. In light of this, we present a distributed IoT system that integrates a computer vision pipeline and object detection capabilities into multiple end-devices, constantly monitoring for the presence of firearms and sharp weapons. Once a weapon is detected, the end-device sends a series of frames to a cloud server that implements a 3DCNN to classify the scene as either a robbery or a normal situation, thus minimizing false positives. The deep learning process to train and deploy weapon detection models uses a custom dataset with 16,799 images of firearms and sharp weapons. The best-performing model, YOLOv5s, optimized using TensorRT, achieved a final mAP of 0.87 running at 4.43 FPS. Additionally, the 3DCNN demonstrated 0.88 accuracy in detecting abnormal situations. Extensive experiments validate that the proposed system significantly reduces false positives while autonomously monitoring multiple locations in real-time.