Theft Detection Implementation Using Machine Learning
S. Murali, J. V. Anchitaalagammai, S. K. Kavitha, S. Krishnakumar, S. Sibiraman, K. Suryakumar · 2024
Previously, crime detection relied exclusively on human observation, using inefficient methods for identifying illicit actions. The inclusion of CCTV cameras significantly improved monitoring, although manually examining video footage remained time-consuming. With advances in Artificial Intelligence (AI) and Machine Learning (ML), creating intelligent systems to automate crime detection in CCTV monitoring has become critical. These systems not only detect and classify crimes, but also send alerts to surrounding police stations and medical facilities, so helping to reduce crime rates. Object recognition and tracking in computer vision have received attention due to their importance in surveillance and security systems. This study describes a system that improves security by doing real-time object detection on live video streams, using machine learning methods such as Support Vector Machines (SVM) and XGBoost for precise classification. By incorporating specialized hardware, the system can be further refined for greater accuracy and efficiency in crime detection, allowing for faster response times to possible threats. Keywords--Crime Detection, CCTV Surveillance, Artificial Intelligence (AI), Machine Learning (ML), Support Vector Machines (SVM), XGBoost, Security Systems.