Analysis of Object Detection, Prediction, and Prevention AI and ML algorithms for Enhancing Crime Surveillance Videos

This research paper aims to propose a system for object detection and prediction in video files for crime prediction and detection using AI and ML techniques. With the increase in crime rates globally, it has become essential to develop an efficient and effective system for crime prevention and detection. The proposed system uses deep learning algorithms, such as YOLOv5 and Faster R-CNN, for object detection in videos. Support Vector Machines, Decision Trees or Random Forest machine learning algorithms are used to classify the detected objects for crime prediction. The proposed system is implemented and tested on a dataset of surveillance videos, and the results show promising accuracy in crime prediction and detection. The system can be useful for law enforcement agencies in identifying potential criminal activities, taking necessary preventive measures, and reducing the crime rate. The implementation of the system can also be extended to other domains, such as traffic surveillance, wildlife monitoring, and industrial safety.