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Adaptive Traffic Light Detection using Color Spaces

by Aradhana Verma, C.S. Yadav, Pradeep Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 173 - Number 4
Year of Publication: 2017
Authors: Aradhana Verma, C.S. Yadav, Pradeep Kumar
10.5120/ijca2017915291

Aradhana Verma, C.S. Yadav, Pradeep Kumar . Adaptive Traffic Light Detection using Color Spaces. International Journal of Computer Applications. 173, 4 ( Sep 2017), 33-34. DOI=10.5120/ijca2017915291

@article{ 10.5120/ijca2017915291,
author = { Aradhana Verma, C.S. Yadav, Pradeep Kumar },
title = { Adaptive Traffic Light Detection using Color Spaces },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 173 },
number = { 4 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 33-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume173/number4/28326-2017915291/ },
doi = { 10.5120/ijca2017915291 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:20:23.316951+05:30
%A Aradhana Verma
%A C.S. Yadav
%A Pradeep Kumar
%T Adaptive Traffic Light Detection using Color Spaces
%J International Journal of Computer Applications
%@ 0975-8887
%V 173
%N 4
%P 33-34
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a color space based algorithm for traffic signal light detection for the modules used in Advanced Driver Assistance Systems (ADAS). The autonomous vehicle has been a topic for the discussions among the computer science engineers for several years. Traffic Signal Light Detection algorithm is based on color space theory which efficiently detects the color of traffic light during day time as well as night time i.e. weather invariant..

References
  1. V. Anh, A. Ramanandan, C. Anning, J. A. Farrell, and M. Barth, "Real-Time Computer Vision/DGPS-Aided Inertial Navigation System for Lane-Level Vehicle Navigation," Intelligent Transportation Systems, IEEE Transactions on, vol. 13, pp. 899-913, 2012.
  2. Z. Cai, M. Gu, and Y. Li, "Real-time arrow traffic light recognition system for intelligent vehicle," Las Vegas, NV, 2012, pp. 848-854.
  3. M. Diaz-Cabrera, P. Cerri, and J. Sanchez-Medina, "Suspended traffic lights detection and distance estimation using color features," Anchorage, AK, 2012, pp. 1315-1320.
  4. J. Gong, Y. Jiang, G. Xiong, C. Guan, G. Tao, and H. Chen, "The recognition and tracking of traffic lights based on color segmentation and CAMSHIFT for intelligent vehicles," La Jolla, CA, 2010, pp. 431-435.
  5. M. Omachi and S. Omachi, "Detection of traffic light using structural information," Beijing, 2010, pp. 809-812.
  6. Y. Shen, U. Ozguner, K. Redmill, and J. Liu, "A robust video based traffic light detection algorithm for intelligent vehicles," Xi'an, 2009, pp. 521-526.
  7. H. Tae-Hyun, J. In-Hak, and C. Seong-Ik, "Detection of traffic lights for vision-based car navigation system," vol. 4319 LNCS, ed. Hsinchu, 2006, pp. 682-691.
  8. BoWu and Ram Nevatia, “Detection and Segmentation of Multiple, Partially Occluded Objects by Grouping, Merging, Assigning Part Detection Responses,” International Journal of Computer Vision, vo. 82, pp. 185-204, 2009.
  9. Wen-Chang Cheng and Ding-MaoJhan, “A self-constructing cascade classifier with AdaBoost and SVM for pedestrian detection,” Engineering Applications of Artificial Intelligence, vol. 26, pp. 1016–1028, 2013.
  10. Rodrigo Verschae, Javier Ruiz-del-Solar and Mauricio Correa, “A unified learning framework for object detection and classification using nested cascades of boosted classifiers,” Machine Vision and Applications, vol. 19, pp. 85–103, 2008.
  11. Joo Kooi Tan, Kazuki Inumaru, Seiji Ishikawa, and Takashi Morie, “Automatic detection of pedestrians from stereo camera images,” Artif Life Robotics, vol. 15, pp. 459–463, 2010.
  12. Gianluca Antonini, Santiago Venegas Martinez, Michel Bierlaire, and Jean Philippe Thiran, “Behavioral Priors for Detection and Tracking of Pedestrians in Video Sequences,” International Journal of Computer Vision, vol. 69(2), pp. 159–180, 2006.
  13. Azra Habibovic, Emma Tivesten, Nobuyuki Uchida, Jonas Bärgman, and Mikael Ljung Austa, “Driver behavior in car-to-pedestrian incidents: an application of the Driving Reliability and Error Analysis Method (DREAM),” Accident Analysis and Prevention, vol. 50, pp. 554– 565, 2013.
  14. Yu-Ting Chen and Chu-Song Chen, “Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages,” IEEE Transactions on Image Processing, vol. 17 (8), pp. 1452-1464, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

ADAS Autonomous Vehicle Theory TSLD Color Space

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