Determining vehicle speed based on video using convolutional neural network
Alexander Grents, Vitalii Varkentin, Nikolay Goryaev
Abstract
The present study solves the issue of estimating traffic flows based on video surveillance camera data. The goal is to count and classify vehicles and determine their speed. This subject area is at an early stage of development, and the study focuses on an intersection in the city of Chelyabinsk, Russia. We used a two-stage Faster R-CNN detector together with a SORT tracker to solve the set task. For the purposes of detector training and evaluation, we collected 750 video frames from over 52,000 objects located in various conditions. Experimental results show that the proposed system can count vehicles, classify them, and determine their speed with an average absolute percentage error not exceeding 22%. The data set presented in the study can further be used by other researchers as a complex test or additional training data.