Vehicle detection based on And–Or Graph and Hybrid Image Templates for complex urban traffic conditions

2015 ◽  
Vol 51 ◽  
pp. 19-28 ◽  
Author(s):  
Ye Li ◽  
Fei-Yue Wang
Keyword(s):  
Vehicle Detection ◽  
Urban Traffic ◽  
Traffic Conditions ◽  
Image Templates

scholarly journals Robust Vehicle Detection and Counting Algorithm Adapted to Complex Traffic Environments with Sudden Illumination Changes and Shadows

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2686 ◽  
Author(s):  
Yue Chen ◽  
Wusheng Hu
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Detection Method ◽  
Vehicle Detection ◽  
Urban Traffic ◽  
Traffic Information ◽  
Access To Information ◽  
Traffic Conditions ◽  
Illumination Changes ◽  
Detection And Counting

The real-time vehicle detection and counting plays a crucial role in traffic control. To collect traffic information continuously, the access to information from traffic video shows great importance and huge advantages compared with traditional technologies. However, most current algorithms are not adapted to the effects of undesirable environments, such as sudden changes in illumination, vehicle shadows, and complex urban traffic conditions, etc. To address these problems, a new vehicle detection and counting method was proposed in this paper. Based on a real-time background model, the problem of sudden illumination changes could be solved, while the vehicle shadows could be removed using a detection method based on motion. The vehicle counting was built on two types of ROIs—called Normative-Lane and Non-Normative-Lane—which could adapt to the complex urban traffic conditions, especially for non-normative driving. Results have shown that the methodology we proposed is able to count vehicles with 99.93% accuracy under the undesirable environments mentioned above. At the same time, the setting of the Normative-Lane and the Non-Normative-Lane can realize the detection of non-normative driving, and it is of great significance to improve the counting accuracy.

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.

Video-based vehicle detection and classification in heterogeneous traffic conditions using a novel kernel classifier

2013 ◽  
Vol 59 (5) ◽  
pp. 541 ◽  
Author(s):  
PradeepKumar Mishra ◽  
Ajay Nandoriya ◽  
Subhasis Chaudhuri ◽  
Mohamed Athiq
Keyword(s):  
Vehicle Detection ◽  
Heterogeneous Traffic ◽  
Traffic Conditions

A hierarchical clustering of features approach for vehicle tracking in traffic environments

2016 ◽  
Vol 9 (4) ◽  
pp. 354-368 ◽  
Author(s):  
Anan Banharnsakun ◽  
Supannee Tanathong
Keyword(s):  
Hierarchical Clustering ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Corner Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Night Time ◽  
Content Type ◽  
Traffic Conditions ◽  
Detection And Tracking

Purpose Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking. Especially in a traffic video monitoring system, vehicle detection is an essential and challenging task. In the previous studies, many vehicle detection methods have been presented. These proposed approaches mostly used either motion information or characteristic information to detect vehicles. Although these methods are effective in detecting vehicles, their detection accuracy still needs to be improved. Moreover, the headlights and windshields, which are used as the vehicle features for detection in these methods, are easily obscured in some traffic conditions. The paper aims to discuss these issues. Design/methodology/approach First, each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model. Next, the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks. These feature blocks will be used to track the moving objects frame by frame. Findings Using the proposed method, it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement (waving trees), which has to be deemed as background. In addition, the proposed method is able to deal with different vehicle shapes such as cars, vans, and motorcycles. Originality/value This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.

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

Nighttime Motion Vehicle Detection Based on Multiple Instance Learning

2014 ◽  
Vol 529 ◽  
pp. 370-374
Author(s):  
Shao Ping Zhu
Keyword(s):  
Vehicle Detection ◽  
Learning Model ◽  
Multiple Instance Learning ◽  
Urban Traffic ◽  
Detection Accuracy ◽  
Road Detection ◽  
Traffic Condition ◽  
Sift Feature ◽  
Moving Vehicles ◽  
Label Information

In this paper, we propose an effective approach for detecting moving vehicles in nighttime traffic scenes. We use Multiple Instance Learning method to automatically detect vehicle from video sequences by constructing the Multiple Instance Learning model at nighttime. At first, we extract SIFT feature using SIFT feature extraction algorithm, which is used to characterize moving vehicles at nighttime. Then Multiple Instance Learning model is used for the on-road detection of vehicles at nighttime, in order to improve the detection accuracy, the class label information was used for the learning of the Multiple Instance Learning model. Final experiments were performed and evaluate the proposed method at nighttime under urban traffic condition, the experiment results show that the average detection accuracy is over 96.2%, which validates that the proposed vehicle detection approach is feasible and effective for the on-road detection of vehicles at nighttime and identification in various nighttime environments.

Sign in / Sign up

Export Citation Format

Share Document