Comparative analysis of vehicle detection in urban traffic environment using Haar cascaded classifiers and blob statistics

2016 ◽  
Author(s):  
Yumnah Hasan ◽  
Muhammad Umair Arif ◽  
Amad Asif ◽  
Rana Hammad Raza
Keyword(s):  
Comparative Analysis ◽  
Vehicle Detection ◽  
Urban Traffic ◽  
Cascaded Classifiers ◽  
Traffic Environment

An Energy-Efficient Vehicle Detection Algorithm for a Complex Urban Traffic Environment

CICTP 2015 ◽  
2015 ◽  
Author(s):  
Han He ◽  
Tao Jiang ◽  
Hongpeng Zhao ◽  
Jiangchen Li ◽  
Tony Z. Qiu ◽  
...  
Keyword(s):  
Energy Efficient ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Urban Traffic ◽  
Traffic Environment

scholarly journals Obstacle Detection in Cluttered Traffic Environment Based on Candidate Generation and Classification

2006 ◽  
Vol 1 (4) ◽  
pp. 93 ◽  
Author(s):  
Qing Li ◽  
F.C. Sun
Keyword(s):  
Vehicle Detection ◽  
Obstacle Detection ◽  
Experimental Results ◽  
Geometrical Features ◽  
Traffic Environment ◽  
Novel Method ◽  
Shadow Cast

A novel method to detect vehicles is presented in the paper. Assumption of the vehicle is made using the geometrical features of the vehicle rear by the statistical histogram. Then hypothesis is verified using the property of the shadow cast by the car according to a prior acknowledgement of traffic scene. Finally, the vehicle detection is realized by hypothesis and verification of objects. The experimental results show the efficiency and feasibility of the method.

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

scholarly journals Shadow-Based Vehicle Detection in Urban Traffic

Sensors ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 975 ◽  
Author(s):  
Manuel Ibarra-Arenado ◽  
Tardi Tjahjadi ◽  
Juan Pérez-Oria ◽  
Sandra Robla-Gómez ◽  
Agustín Jiménez-Avello
Keyword(s):  
Vehicle Detection ◽  
Urban Traffic
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