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Traffic light recognition for autonomous vehicles by admixing the traditional ML and DL
Author(s): Kumar Vishal; C. S. Arvind; Ritesh Mishra; Venugopal Gundimeda
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Paper Abstract

Traffic light recognition (TLR) is an integral component of an intelligent vehicle and advance driver assistance systems (ADAS). At present, most of TLR solutions use vision based system along with prior knowledge of traffic light position (map information and height) provided by supporting sensors like Global Positioning System (GPS) sensor, to obtained high accuracy. In this work, we present a method that performs a real time TLR using only vision sensor and achieve good results. Our TLR process is divided into three stages, viz., traffic light box (TLB) detection, extraction of the glowing area from traffic light box and classification. Here, traffic light box detection is carried out using state-ofthe-art real-time object detection method, You Only Look Once (YOLO). For extraction, we project traffic light box region of interest (ROI) to custom color space and perform the blob analysis. In order to elimination false positives, we introduce light weight efficient classifier model in custom color space. For traffic light states classification, we use support vector machine (SVM) with RGB histogram of the cropped ROI as a feature. Bosch Small Traffic Lights Dataset has been used for the empirical validation of our method and achieving F1 score of 0.94 as a performance benchmark.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410H (15 March 2019); doi: 10.1117/12.2523105
Show Author Affiliations
Kumar Vishal, Cognizant Technology Solutions India Private Ltd. (India)
C. S. Arvind, Cognizant Technology Solutions India Private Ltd. (India)
Ritesh Mishra, Cognizant Technology Solutions India Private Ltd. (India)
Venugopal Gundimeda, Cognizant Technology Solutions India Private Ltd. (India)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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