This project outlines a monitoring system designed to enhance road
safety by detecting bikers who do not wear helmets and identifying
triple rides. The system promotes advanced technologies such as computer
vision and automated learning to monitor traffic conditions and
effectively enforce safety regulations.
Overview of the system
- Configuration:
High-resolution cameras are installed at strategic locations such as
improvised intersections and routes for real-time footage of bikers.
- Photo
processing: Photographed images are processed using computer vision
algorithms to identify bikers and the system analyses features such as
head and number of passengers on the bike.
- Helmet detection:
Using automated learning models, the system distinguishes between helmet
and helmet-free passengers. Non-compliance alerts are generated.
- Disclosure
of the triple pier: the system also determines cases of triple traffic
by counting the number of individuals on a single bike. This is critical
to the enforcement of safety regulations.
- Data analysis:
Compiled data are being analysed to identify trends and hot spots in
relation to violations, and to assist in the implementation of targeted
campaigns and public awareness.
- Real-time notices: Law
enforcement agencies receive real-time notices of violations, allowing
immediate action to ensure compliance with safety regulations.
With
the implementation of this surveillance system, we aim to significantly
reduce incidents and to promote safer bike ride practices.