Next Article in Journal
The Improvement of Reserve Polysaccharide Glycogen Level and Other Quality Parameters of S. cerevisiae Brewing Dry Yeasts by Their Rehydration in Water, Treated with Low-Temperature, Low-Pressure Glow Plasma (LPGP)
Previous Article in Journal
A Brief Review of Machine Learning-Based Bioactive Compound Research
Previous Article in Special Issue
Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
 
 
Article

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

1
Technological Institute of Culiacan, Culiacan 80014, Sinaloa, Mexico
2
Faculty of Civil Engineering, Conacyt-Universidad Michoacana de San Nicolás de Hidalgo, Morelia 58004, Michoacán, Mexico
3
Higher Technological Institute of Libres, Libres 89930, Puebla, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Paweł Droździel, Radovan Madleňák, Saugirdas Pukalskas, Drago Sever and Marcin Ślęzak
Appl. Sci. 2022, 12(6), 2907; https://doi.org/10.3390/app12062907
Received: 31 January 2022 / Revised: 23 February 2022 / Accepted: 28 February 2022 / Published: 11 March 2022
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. View Full-Text
Keywords: statistical regression; kalman filter; vehicle tracking; vehicle speed estimation; YOLO statistical regression; kalman filter; vehicle tracking; vehicle speed estimation; YOLO
Show Figures

Figure 1

MDPI and ACS Style

Rodríguez-Rangel, H.; Morales-Rosales, L.A.; Imperial-Rojo, R.; Roman-Garay, M.A.; Peralta-Peñuñuri, G.E.; Lobato-Báez, M. Analysis of Statistical and Artificial Intelligence Algorithms for Real-Time Speed Estimation Based on Vehicle Detection with YOLO. Appl. Sci. 2022, 12, 2907. https://doi.org/10.3390/app12062907

AMA Style

Rodríguez-Rangel H, Morales-Rosales LA, Imperial-Rojo R, Roman-Garay MA, Peralta-Peñuñuri GE, Lobato-Báez M. Analysis of Statistical and Artificial Intelligence Algorithms for Real-Time Speed Estimation Based on Vehicle Detection with YOLO. Applied Sciences. 2022; 12(6):2907. https://doi.org/10.3390/app12062907

Chicago/Turabian Style

Rodríguez-Rangel, Héctor, Luis Alberto Morales-Rosales, Rafael Imperial-Rojo, Mario Alberto Roman-Garay, Gloria Ekaterine Peralta-Peñuñuri, and Mariana Lobato-Báez. 2022. "Analysis of Statistical and Artificial Intelligence Algorithms for Real-Time Speed Estimation Based on Vehicle Detection with YOLO" Applied Sciences 12, no. 6: 2907. https://doi.org/10.3390/app12062907

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop