Nirikshak marks a groundbreaking advancement in traffic management and road safety. Developed for a government client, this innovative solution uses a simple smartphone to accurately measure vehicle speeds and detect overspeeding. By harnessing advanced machine learning models and a seamless Android app interface, Nirikshak aims to significantly reduce road accidents and traffic violations, setting a new standard in traffic law enforcement technology.
The primary challenge was to accurately measure vehicle speeds using only a smartphone, a feat never before accomplished in this domain. The project demanded high precision in speed estimation, robust vehicle identification in varied traffic conditions, and an intuitive user interface for efficient operation.
Achieving 95% accuracy in speed estimation
Unique vehicle identification in dense traffic
Designing a user-friendly and error-resistant interface
Our approach involved creating an intricate algorithm that analyzes video clips and timestamps to determine vehicle speed with exceptional accuracy. This required extensive research and testing of various neural networks for object detection and tracking.
Developed an optical flow algorithm for speed detection
Researched multiple neural networks for object tracking
Implemented advanced OCR for number plate recognition
Engineered anomaly detection for lane switching
Created a user-friendly app interface with guidance features
Nirikshak set a new benchmark in road safety, enhancing traffic law enforcement, and promoting responsible driving.
95% accuracy in speed detection
Enhanced road safety measures
Real-time vehicle identification
By bridging the gap between complex medical data and intuitive access, we’ve empowered healthcare professionals to make better decisions faster, ultimately improving patient outcomes.
