• Rashmi Borase
• Vaishnavi Kulkarni
• Omkar Nikhal
• Sumeet Singh
• Madhuri Mane

Speed detection and analysis of traffic data is a challenging task that plays a vital role in the safety of civilians considering the dangers imposed with everyday transportation. The presence of an automated and intelligent traffic surveillance system in today’s civilized society is of critical importance for monitoring traffic activity and detecting overspeeding vehicles. We present an accurate and effective computer vision-based system for monitoring vehicular traffic by detecting and classifying vehicles and extracting crucial statistics such as average road speed, overspeeding detections, vehicle types, frequency of vehicles, etc. Our proposed system implements a machine learning-based approach that makes use of computer vision techniques and classifiers on real-world traffic video surveillance for effective vehicular detection and classification as well as for obtaining traffic statistics.

Vehicle Speed Estimation, Computer Vision, Traffic Video Analysis, Vehicle Detection, Machine Learning

"An Ensemble Based Deep Network for Vehicle Detection, Classification and Speed Estimation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.b743-b748, July-2022, Available :http://www.jetir.org/papers/JETIR2207191.pdf

"An Ensemble Based Deep Network for Vehicle Detection, Classification and Speed Estimation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppb743-b748, July-2022, Available at : http://www.jetir.org/papers/JETIR2207191.pdf