Analysis of Statistical and Artificial Intelligence Algorithms for Real-Time Speed Estimation Based on Vehicle Detection with YOLO

Abstract

Automobiles have increased urban mobility, but traffic accidents have also increased. Therefore, road safety is a significant concern involving academics and government. Transit studies are the main supply for studying road accidents, congestion, and flow traffic, allowing the understanding of traffic flow. They require special equipment (sensors) to measure the car’s speed. With technological advances, artificial intelligence, and videos, it is possible to estimate the speed in real-time without modifying the installed urban infrastructure. We need to employ public databases that provide reliable monocular videos to generate automated traffic studies. The problem of speed estimation with a monocular camera involves synchronizing data recording, tracking, and detecting the vehicles over the road considering the lanes and distance between cars. Usually, a set of constraints are considered, such as camera calibration, flat roads, including methods based on the homography and augmented intrusion lines, patterns or regions, or prior knowledge about the actual dimensions of some of the objects. In this paper, we present a system that generates a dataset from videos recorded from a highway—obtaining 532 samples; we separated the vehicle’s detection by lane, estimating its speed. We use this data set to compare five different statistical methods and three machine learning methods to evaluate their accuracy in estimating the cars’ speed in real-time. Our vehicle estimation requires a feature extraction process using YOLOv3 and Kalman filter to detect and track vehicles. The Linear Regression Model (LRM) yielded the best results obtaining a Mean Absolute Error (MAE) of 1.694 km/h for the center lane and 0.956 km/h for the last lane. The results were compared with several state-of-the-art works, having competitive performance. Hence, LRM is fast estimating speed in real time and does not require high computational resources allowing a future hardware implementation.