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.

Keywords: Avoid Accident, CCTV, Classification, CNN, CPR, Desktop application, Frames, Helmet Detection, Law Enforcement, License-plate Recognition, Machine Learning, Noise removal, Object detection, Object localization, Preprocessing, Segmentation, Surveillance, SVM, Traffic Rule, Video.

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