Amir Sallach Khan

Academic

Career

Verbatim text

Dynamic Whistle Blower Traffic Monitoring System 
This project aims to design and implement a Dynamic Whistleblower Traffic Monitoring System, 
an ML & loT-powered solution to enhance traffic law enforcement and road safety. Speeding is a 
major cause of road accidents, yet the absence of real-time, automated monitoring systems leads 
to increased violations and fatalities. This project leverages ML, loT, and cloud computing to provide 
an efficient and scalable approach to speed detection and enforcement. 
The system integrates 2D LIDAR sensors, high-resolution cameras, and machine learning models to 
detect speeding vehicles, recognize number plates, and transmit data to a cloud-based dashboard 
for automated violation reporting. By ensuring accuracy, efficiency, and real-time enforcement, this 
solution aims to reduce road accidents, improve compliance, and lay the foundation for smart, ML 
& loT-driven traffic management in the future. 
Technology Used: 
Internet of Things, Machine Learning, 
Cloud Computing, Embedded Systems. 
Supervisor Name: 
Dr. Muhammad Tariq 
Group Members: 
Amir Sallach Khan (i21 - 0962) 
Muhammad Talha Nadeem (i19 - 0802) 
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