Enhanced Crime Prediction with Computer Vision-Yolov4 Approach (Global Journal of Computer Science and Technology, 2024)
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 The model effectively identified potential criminals as High-Risk Persons emphasizing its efficacy in predicting high-risk behaviors
Keywords: deep learning; machine learning; yolov4 detector; convolutional neural network (CNN)