Enhanced Crime Prediction with Computer Vision-Yolov4 Approach

Abstract

This research paper presents the development of an artificial intelligence safety application on an HP Pavilion gaming machine, utilizing criminal footage from reputable databases like the UCF-Crime open-source dataset. The system underwent meticulous data annotation to identify five distinct classes crucial for anomaly detection: Person, Short Gun, Handgun, Knife, and Rifle. Supervised machine learning techniques were applied, focusing on monitoring human trajectories and employing deep-SORT and Euclidean distance computations to track individuals, simulating real-world crime scenarios. The AI safety model showcased outstanding performance with an average precision rate of approximately 86.43%, exceeding 90% after 2000 iterations, demonstrating versatility across all categories with notable average precision accuracies for rifles (98.90%), handguns (96.93%), and knives (97.66%). Enhancements to the Python script improved the system's ability to detect weapons sub-objects in human subjects and classify potential perpetrators as high risk, a novel aspect of this study.