Helmet Violation Processing for Law Enforcement using CCTV Camera

The rising number of traffic rule violators, as well as the resulting mishaps, has made road safety a major concern. Detection of traffic rule violators is challenging due to various difficulties such as occlusion, illumination, poor quality of surveillance video, varying weather conditions, etc. The existing surveillance systems are primarily dependent on the performance of human operators who are unrealistically expected to simultaneously watch many screens that show streams captured by different cameras. The task of these operators is becoming more difficult as the number of simultaneous video streams to watch increases. It is well-known that after twenty minutes of continuous work, the operator's attention degrades significantly. Thus, there is a need for an automatic detection system. This research aims to increase the installations of new cameras covering more areas. It will help overcome all the difficulties mentioned above and ensure motorcyclists' detection without helmets using the CCTV cameras' footage and machine learning algorithms. It will help law enforcement by police and eventually change the risk behaviors of motorcyclists. Consequently, the number of accidents and their severity will reduce.