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A Scheduling Plan Model for Metro Crew Incorporating Fatigue and Biological Rhythms Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Traffic Lights Recognition Based on Position Feature

Authors : Zhi-fa Yang, Xian-jun Fan, Zhuo Yu, Shi-wu Li, Ai-min liu, Chang-an song

Published in: Green Transportation and Low Carbon Mobility Safety

Publisher: Springer Nature Singapore

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Abstract

Due to the color and shape characteristics of traffic lights, the color model and shape detection are used to detect traffic lights in the work, the images were processed by the ROI (region of interest) extraction, image enhancement, grayscale binarization processing and morphological processing. Then the contour search and connected domain filtering algorithm were used to extract the traffic signal backlight backplane area, thus detecting and segmenting the traffic signal light backplane. Moreover, taking the traffic signal light backplane as positive sample, the other non-traffic light backplane was used as negative sample to build model library. HOG algorithm was used to extract the feature vectors of samples and exclude the false targets based on SVM classification algorithm. Finally, according to the positions of red and green signal lights on signal light, the pixel value accumulation in the area where the signal light is located was calculated as position feature to recognize the red and green signal lights.

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Metadata
Title
Traffic Lights Recognition Based on Position Feature
Authors
Zhi-fa Yang
Xian-jun Fan
Zhuo Yu
Shi-wu Li
Ai-min liu
Chang-an song
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Green Transportation and Low Carbon Mobility Safety

Print ISBN: 978-981-19-5614-0

Electronic ISBN: 978-981-19-5615-7

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-5615-7_9

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