Detection Of Criminal Activities/Criminal Through CCTV By Live Footage Analysis
Rahul Kumar, Divyajeet Singh Rajpurohit, Mushraf Hanief, Shilpi Burman Sharma, Tanupriya Choudhury, Tanupriya Choudhury, Ayan Sar · 2024
The integration of deep learning techniques, particularly the You Only Look Once (YOLO) object detection model, with Closed-Circuit Television (CCTV) systems has significantly transformed the landscape of crime detection and prevention. This research paper provides a comprehensive analysis of YOLO-based CCTV crime detection, elucidating the innovative strategies, challenges, and potential implications for enhancing public safety. The YOLO framework is well-known for its real-time object detection capabilities, which enable it to swiftly and accurately identify multiple objects within a single image frame. In the context of CCTV surveillance, YOLO has proven invaluable in the identification and tracking of criminal activities. The paper explores how YOLO's object detection architecture is adapted to the unique challenges of CCTV crime detection, including variable lighting conditions, occlusions, and diverse camera angles. One of the fundamental contributions of this research is the development of a YOLO-based CCTV crime detection system. This system combines YOLO's speed and accuracy with a sophisticated backend for video analysis and real-time alerting. The system not only detects suspicious activities but also classifies them into different categories, such as theft, vandalism, or violent behavior, thus facilitating immediate responses from law enforcement agencies. Moreover, the paper delves into the application of deep learning in data pre-processing, noise reduction, and the handling of extensive video feeds. Techniques such as transfer learning and data augmentation are explored to enhance the model's generalization and adaptability to different surveillance environments. The scalability of the system is also discussed, allowing it to be deployed in urban centers, commercial spaces, and residential areas. While the advantages of YOLO-based CCTV crime detection are evident, this research also highlights potential challenges, including privacy concerns, ethical considerations, and the need for efficient hardware resources. Striking a balance between public safety and individual privacy remains a critical point of discussion.