• Dr. A. Prakash
• R. Ashwin
• M. Bhuvaneshwari
• M. Chandhanu

In intelligent transportation systems, vehicle speed estimation plays a crucial role in infrastructure optimisation, safety, and traffic control. This work investigates the use of deep learning methods with Open CV to estimate vehicle speeds from video feeds in real-time and with high accuracy. The suggested method uses a Convolutional Neural Network (CNN) to recognise and follow automobiles in the video frames automatically. Next, by examining the displacement of identified vehicles over a series of frames, speed estimation is accomplished. To achieve accurate and efficient vehicle detection, the solution makes use of the YOLO (You Only Look Once) object detection paradigm. The system is more adaptive to changing traffic scenarios and environmental factors when deep learning techniques are used. The suggested approach's efficacy in delivering dependable vehicle speed estimations is evidenced by the experimental findings, which also highlight its potential for use in intelligent transportation systems and traffic monitoring applications.

Vehicle Speed Estimation Profound Learning OpenCV Convolutional Neural Systems (CNNs) YOLO (You Simply See Once) Question Location Brilliantly Transportation Frameworks Activity Checking Real-time Speed Estimation Computer Vision Framework Optimization 

"Vehicle Speed Estimation using deep learning with OpenCV", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.h595-h598, March-2024, Available :http://www.jetir.org/papers/JETIR2403777.pdf

"Vehicle Speed Estimation using deep learning with OpenCV", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. pph595-h598, March-2024, Available at : http://www.jetir.org/papers/JETIR2403777.pdf