CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS

被引：2

作者：

Pamula, Teresa ^{[1
]}

机构：

[1] Silesian Tech Univ, Fac Transport, Krasinskiego 8 St, PL-40019 Katowice, Poland

来源：

SCIENTIFIC JOURNAL OF SILESIAN UNIVERSITY OF TECHNOLOGY-SERIES TRANSPORT | 2016年 / 92卷

关键词：

traffic conditions; textures features; neural networks;

D O I：

10.20858/sjsutst.2016.92.10

中图分类号：

U [交通运输];

学科分类号：

08 ; 0823 ;

摘要：

The paper presents a method of classification of road traffic conditions based on the analysis of the content of images of the traffic flow. The view of the traffic lanes with vehicles is treated as a texture, while the change in the description of its characteristics is ascribed to the change in the density of traffic. Four classes of conditions are determined on the basis of the values of Haralick texture features. An MLP network is used for classification. Video data, which were registered by an UAV hanging over a traffic junction, are used for validation of the method.

引用

下载

页码：101 / 109

页数：9

共 50 条

[31] Traffic Flow Forecast of Road Networks with Recurrent Neural Networks
Fern Universitat in Hagen, Germany
Proc. - Int. Symp. Comput. Sci. Intell. Controls, ISCSIC, 1600, (31-38):
[32] Automatic Traffic Monitoring Using Neural Networks from Satellite Images
Eslami, Mehrad
Faez, Karim
11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), 2010, : 1616 - 1621
[33] Learning Traffic as Images for Incident Detection Using Convolutional Neural Networks
Liu, Xiaozhou
Cai, Hengxing
Zhong, Renxin
Sun, Weili
Chen, Junzhou
IEEE ACCESS, 2020, 8 : 7916 - 7924
[34] Road Traffic Monitoring from UAV Images Using Deep Learning Networks
Byun, Sungwoo
Shin, In-Kyoung
Moon, Jucheol
Kang, Jiyoung
Choi, Sang-Il
REMOTE SENSING, 2021, 13 (20)
[35] Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks
Ko, Taejin
Raza, Syed M.
Dang Thien Binh
Kim, Moonseong
Choo, Hyunseung
PROCEEDINGS OF THE 2020 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM), 2020,
[36] VEHICLE ACCIDENT AND TRAFFIC CLASSIFICATION USING DEEP CONVOLUTIONAL NEURAL NETWORKS
Kumeda, Bulbula
Zhang Fengli
Oluwasanmi, Ariyo
Owusu, Forster
Assefa, Maregu
Amenu, Temesgen
2019 16TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIVE MEDIA TECHNOLOGY AND INFORMATION PROCESSING (ICWAMTIP), 2019, : 323 - 328
[37] Internet Traffic Classification Using an Ensemble of Deep Convolutional Neural Networks
Shahraki, Amin
Abbasi, Mahmoud
Taherkordi, Amir
Kaosar, Mohammed
PROCEEDINGS OF THE 4TH FLEXNETS WORKSHOP ON FLEXIBLE NETWORKS, ARTIFICIAL INTELLIGENCE SUPPORTED NETWORK FLEXIBILITY AND AGILITY (FLEXNETS'21), 2021, : 38 - 43
[38] A new method for traffic signs classification using probabilistic neural networks
Zhang, Hang
Luo, Dayong
ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 3, PROCEEDINGS, 2006, 3973 : 33 - 39
[39] Cause Analysis and Accident Classification of Road Traffic Accidents Based on Complex Networks
Wang, Yongdong
Zhai, Haonan
Cao, Xianghong
Geng, Xin
APPLIED SCIENCES-BASEL, 2023, 13 (23):
[40] Predicting Citywide Road Traffic Flow Using Deep Spatiotemporal Neural Networks
Jia, Tao
Yan, Penggao
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 22 (05) : 3101 - 3111

← 1 2 3 4 5 →