Computer Science ›› 2018, Vol. 45 ›› Issue (6): 314-319.doi: 10.11896/j.issn.1002-137X.2018.06.055

• Graphics, Image & Pattern Recognition • Previous Articles    

Real-time Detection and Recognition of Traffic Light Based on Time-Space Model

LI Zong-xin, QIN Bo, WANG Meng-qian   

  1. Department of Computer Science & Technology,Ocean University of China,Qingdao,Shandong 266100,China
  • Received:2016-12-18 Online:2018-06-15 Published:2018-07-24

Abstract: Detection and recognition of traffic light are important for driverless cars and advanced driver assistance systems(ADAS).In order to satisfy the requirements of traffic light detection and recognition in complex urban environment,a real-time detection and recognition algorithm based on time-space model (TSM) was proposed.It was established based on thetime-space continuous variation relationship of video-frame sequence.The proposed algorithm consists of three parts.The first part is fast image segmentation and compression algorithm based on color,which is used to improve the computational efficiency.Second,time-space model of multi-frame image sequence is introduced to improve the accuracy of detection stage.Third,recognition of traffic lights is achieved by using support vector machine (SVM) with histogram of oriented gradients (HOG) features.Experiment results show that this novel algorithm has strong robustness,high efficiency and accuracy.

Key words: ADAS, Fast image segmentation, Pattern recognition, Time-space model, Traffic light detection

CLC Number: 

  • TP391
[1]LUETTEL T,HIMMELSBACH M,WUENSCHE H J.Autonomous Ground Vehicles-Concepts and a Path to the Future[J].Proceedings of the IEEE,2012,100(5):1831-1839.
[2]ZHAO N,YUAN J B,XU H.Survey on Intelligent Transportation System[J].Computer Science,2014,41(11):7-11.(in Chinese)
赵娜,袁家斌,徐晗.智能交通系统综述[J].计算机科学,2014,41(11):7-11.
[3]BUCH N,VELASTIN S A,ORWELL J.A Review of Computer Vision Techniques for the Analysis of Urban Traffic[J].IEEE Transactions on Intelligent Transportation Systems,2011,12(3):920-939.
[4]MOGELMOSE A,TRIVEDI M M,MOESLUND T B.Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems:Perspectives and Survey[J].IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1484-1497.
[5]YELAL M R,SASI S,SHAFFER G R,et al.Color-Based Signal Light Tracking in Real-Time Video[C]//IEEE International Conference on Video and Signal Based Surveillance(AVSS’06).Sydney:IEEE,2006:67-67.
[6]PARK J H,JEONG C.Real-Time Signal Light Detection[C]//Second International Conference on Future Generation Communication and Networking Symposim(FGCNS’08).Sanya:IEEE,2008:139-142.
[7]CHARETTE R D,NASHASHIBI F.Real Time Visual Traffic Lights Recognition Based on Spot Light Detection and Adaptive Traffic Lights Templates[C]//Intelligent Vehicles Symposium(IV).Shaanxi:IEEE,2009:358-363.
[8]ZHOU X R,YUAN J Z,LIU H Z,et al.Research on Algorithm for Real-time Recognition of Traffic Light Based on HOG Features[J].Computer Science,2014,41(7):313-317.(in Chinese)
周宣汝,袁家政,刘宏哲,等.基于HOG特征的交通信号灯实时识别算法研究[J].计算机科学,2014,41(7):313-317.
[9]MALDONADO-BASCON S,LAFUENTE-ARROYO S,GIL-JIMENEZ P,et al.Road-Sign Detection and Recognition Based on Support Vector Machines[J].IEEE Transactions on Intelligent Transportation Systems,2007,8(2):264-278.
[10]LEVINSON J,ASKELAND J,DOLSON J,et al.Traffic Light Mapping,Localization,and State Detection for Autonomous Vehicles[C]//IEEE International Conference on Robotics and Automation(ICRA).Shanghai:IEEE,2011:5784-5791.
[11]FAIRFIELD N,URMSON C.Traffic Light Mapping and Detection[C]//IEEE International Conference on Robotics and Automation(ICRA).Shanghai:IEEE,2011:5421-5426.
[12]SHEN Y,OZGUNER U,REDMILL K,et al.A Robust Video Based Traffic Light Detection Algorithm for Intelligent Vehicles[C]//Intelligent Vehicles Symposium(IV).Shaanxi:IEEE,2009:521-526.
[13]OMACHI M,OMACHI S.Traffic Light Detection with Color and Edge Information[C]//2nd IEEE International Conference on Computer Science and Information Technology.Beijing:2009:284-287.
[14]ROTERS J,JIANG X,ROTHAUS K.Recognition of Traffic Lights in Live Video Streams on Mobile Devices[J].IEEE Transactions on Circuits & Systems for Video Technology,2011,21(10):1497-1511.
[15]DIAZ-CABRERA M,CERRI P,MEDICI P.Robust Real-Time Traffic Light Detection and Distance Estimation Using a Single Camera [J].Expert Systems with Applications,2015,42(8):3911-3923.
[16]GONG J,JIANG Y,XIONG G,et al.The Recognition and Tracking of Traffic Lights Based on Color Segmentation and Camshift for Intelligent Vehicles[C]//Intelligent Vehicles Symposium.La Jolla,CA:IEEE,2010:431-435.
[17]DIAZ-CABRERA M,CERRI P,SANCHEZ-MEDINA J.Suspended Traffic Lights Detection and Distance Estimation Using Color Features[C]//15th International IEEE Conference on Intelligent Transportation Systems (ITSC).Anchorage,Alaska:IEEE,2012:1315-1320.
[18]KOEN V D S,GEVERS T,SNOEK C.Evaluating Color Descriptors for Object and Scene Recognition [J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,32(9):1582-1596.
[19]MASCETTI S,AHMETOVIC D,GERINO A,et al.Robust Traffic Lights Detection on Mobile Devices for Pedestrians with Visual Impairment[J].Computer Vision & Image Understan-ding,2016,148(C):123-135.
[20]JI Y,YANG M,LU Z,et al.Integrating Visual Selective Attention Model with HOG Features for Traffic Light Detection and Recognition[C]//Intelligent Vehicles Symposium (IV).Seoul:IEEE,2015:280-285.
[1] SUN Wen-yun, JIN Zhong, ZHAO Hai-tao, CHEN Chang-sheng. Cross-domain Few-shot Face Spoofing Detection Method Based on Deep Feature Augmentation [J]. Computer Science, 2021, 48(2): 330-336.
[2] WANG Xin-ping, XIA Chun-ming, YAN Jian-jun. Sign Language Recognition Based on Image-interpreted Mechanomyography and Convolution Neural Network [J]. Computer Science, 2021, 48(11): 242-249.
[3] ZHOU Li-peng, MENG Li-min, ZHOU Lei, JIANG Wei and DONG Jian-ping. Fall Detection Algorithm Based on BP Neural Network [J]. Computer Science, 2020, 47(6A): 242-246.
[4] WANG Jun-qian, ZHENG Wen-xian, XU Yong. Novel Image Classification Based on Test Sample Error Reconstruction Collaborative Representation [J]. Computer Science, 2020, 47(6): 104-113.
[5] ZHANG Chun-xiang, ZHAO Chun-lei, CHEN Chao, LUO Hui. Review of Human Activity Recognition Based on Mobile Phone Sensors [J]. Computer Science, 2020, 47(10): 1-8.
[6] LIU Chang-qi, SHAO Kun, HUO Xing, FAN Dong-yang, TAN Jie-qing. K-means Image Segmentation Algorithm Based on Weighted Quality Evaluation Function [J]. Computer Science, 2019, 46(6A): 158-160.
[7] WANG Nan, SUN Shan-wu. UAV Fault Recognition Based on Semi-supervised Clustering [J]. Computer Science, 2019, 46(6A): 192-195.
[8] QIAN Hong-yi, WANG Li-hua, MOU Hong-lei. Fast Detection and Identification of Traffic Lights Based on Deep Learning [J]. Computer Science, 2019, 46(12): 272-278.
[9] CHEN Han-shen, YAO Ming-hai, CHEN Zhi-hao, YANG Zhen. Efficient Method of Lane Detection Based on Multi-frame Blending and Windows Searching [J]. Computer Science, 2018, 45(10): 255-260.
[10] ZHOU Yu-huan, JIANG Da-wei, GONG Yong and CHEN Cong. Encrypted Data Stream Recognition Based on Data Randomness and ELM [J]. Computer Science, 2017, 44(Z6): 380-384.
[11] REN Yun, CHENG Fu-lin and LI Hong-song. Disparity Estimation Algorithm Based on Frequency Sensitive Three-dimensional Self-organizing Map [J]. Computer Science, 2017, 44(Z11): 225-227.
[12] WANG Dong-sheng, HUANG Chuan-he, HUANG Xiao-peng and NI Qiu-fen. Text Mining Algorithm and Application of Telecom Big Data [J]. Computer Science, 2017, 44(12): 232-238.
[13] LI Wei-lin, WEN Jian and MA Wen-kai. Speech Recognition System Based on Deep Neural Network [J]. Computer Science, 2016, 43(Z11): 45-49.
[14] WANG Min-guang and WANG Zhe. Fuzzy Support Vector Data Description with Centers of Classes Distance [J]. Computer Science, 2016, 43(5): 230-233.
[15] WEN Hao, WEN You-kui and WANG Min. Approach to Text Knowledge Depth Mining Based on Pattern Recognition [J]. Computer Science, 2016, 43(3): 279-284.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!