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Camera system is widely used as a road traffic monitoring nowadays but if using the system as a speed camera, an additional speed sensor is required. In this work, we demonstrate a novel method to estimate speed of vehicle in traffic video without using additional sensor. We implement two speed measurement models including measuring vehicle moving distance in a given unit time and measuring vehicle traveling time in a given unit distance. To get parameters of the models, we define four virtual intrusion lines on the road in camera view. Then, YOLOv3, DeepSORT, GoodFeatureToTrack,and Lucas-Kanade pyramidal optical flow algorithm are implemented together for vehicle detection and tracking while the target vehicle moving on the road. From the tracking data, pixel displacement between two consecutive frames while the vehicle crossing each line is measured as Crossing distance. And number of frame that the vehicle consumes while moving from the first line to the other lines is measured as Traveling time. These two parameters at each intrusion line are used as speed measurement metrics. Solution of the metrics are solved by using tracking data of 20 vehicles at 9 difference ground truth speed measured by a laser speed gun. Then, the metrics are used to estimate speed of 813 vehicles. Our best accuracy is with MAE of 3.38 and RMSE of 4.69 km/h when comparing to their ground truth speed. The same dataset are tested on a Multilayer Perceptron Neural Network model. It can reach accuracy with MAE of 3.07 km/h (RMSE 3.98 km/h).

Video-based vehicle speed estimation using speed measurement metrics