Traffic light recognition for autonomous vehicles by admixing the traditional ML and DL

Kumar Vishal; C. S. Arvind; Ritesh Mishra; Venugopal Gundimeda

doi:10.1117/12.2523105

15 March 2019 Traffic light recognition for autonomous vehicles by admixing the traditional ML and DL

Kumar Vishal, C. S. Arvind, Ritesh Mishra, Venugopal Gundimeda

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110410H (2019) https://doi.org/10.1117/12.2523105
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

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.

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Kumar Vishal, C. S. Arvind, Ritesh Mishra, and Venugopal Gundimeda "Traffic light recognition for autonomous vehicles by admixing the traditional ML and DL", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410H (15 March 2019); https://doi.org/10.1117/12.2523105

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KEYWORDS

Sensors

RGB color model

Image resolution

Unmanned vehicles

Global Positioning System

Image processing

Image sensors

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