Traffic Light Detection using Convolutional Neural Networks and Lidar Data

Tien Wen Yeh; Ssu Yun Lin; Huei Yung Lin; Sheng Wei Chan; Che Tsung Lin; Yan Yu Lin

doi:10.1109/ISPACS48206.2019.8986310

Traffic Light Detection using Convolutional Neural Networks and Lidar Data

Tien Wen Yeh
, Ssu Yun Lin
, Huei Yung Lin
, Sheng Wei Chan
, Che Tsung Lin
, Yan Yu Lin

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

This paper presents a traffic light detection system based on convolutional neural networks and lidar data. The proposed approach contains two stages. In the first detection stage, the map information is adopted to assist the object detection, and two cameras with different focal lengths are used to detect traffic lights at different distances. In the following recognition stage, we combine detector and classifier to deal with the problem of many light states. A dataset is created with urban road scenes in Taiwan. The experiments have demonstrated the advance in traffic light detection and recognition.

Original language	English
Title of host publication	Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728130385
DOIs	https://doi.org/10.1109/ISPACS48206.2019.8986310
State	Published - Dec 2019
Event	2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 - Taipei, Taiwan Duration: 3 Dec 2019 → 6 Dec 2019

Publication series

Name	Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

Conference

Conference	2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
Country/Territory	Taiwan
City	Taipei
Period	3/12/19 → 6/12/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

ADAS
state recognition
traffic light detection

Access to Document

10.1109/ISPACS48206.2019.8986310

Cite this

Yeh, T. W., Lin, S. Y., Lin, H. Y., Chan, S. W., Lin, C. T., & Lin, Y. Y. (2019). Traffic Light Detection using Convolutional Neural Networks and Lidar Data. In Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 Article 8986310 (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISPACS48206.2019.8986310

Yeh, Tien Wen ; Lin, Ssu Yun ; Lin, Huei Yung et al. / Traffic Light Detection using Convolutional Neural Networks and Lidar Data. Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019).

@inproceedings{e0c525329d6f48d38f06029ac3ac11b8,

title = "Traffic Light Detection using Convolutional Neural Networks and Lidar Data",

abstract = "This paper presents a traffic light detection system based on convolutional neural networks and lidar data. The proposed approach contains two stages. In the first detection stage, the map information is adopted to assist the object detection, and two cameras with different focal lengths are used to detect traffic lights at different distances. In the following recognition stage, we combine detector and classifier to deal with the problem of many light states. A dataset is created with urban road scenes in Taiwan. The experiments have demonstrated the advance in traffic light detection and recognition.",

keywords = "ADAS, state recognition, traffic light detection",

author = "Yeh, \{Tien Wen\} and Lin, \{Ssu Yun\} and Lin, \{Huei Yung\} and Chan, \{Sheng Wei\} and Lin, \{Che Tsung\} and Lin, \{Yan Yu\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 ; Conference date: 03-12-2019 Through 06-12-2019",

year = "2019",

month = dec,

doi = "10.1109/ISPACS48206.2019.8986310",

language = "???core.languages.en\_GB???",

series = "Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019",

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Yeh, TW, Lin, SY, Lin, HY, Chan, SW, Lin, CT & Lin, YY 2019, Traffic Light Detection using Convolutional Neural Networks and Lidar Data. in Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019., 8986310, Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, Institute of Electrical and Electronics Engineers Inc., 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, Taipei, Taiwan, 3/12/19. https://doi.org/10.1109/ISPACS48206.2019.8986310

Traffic Light Detection using Convolutional Neural Networks and Lidar Data. / Yeh, Tien Wen; Lin, Ssu Yun; Lin, Huei Yung et al.
Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8986310 (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Traffic Light Detection using Convolutional Neural Networks and Lidar Data

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AU - Chan, Sheng Wei

AU - Lin, Che Tsung

AU - Lin, Yan Yu

PY - 2019/12

Y1 - 2019/12

N2 - This paper presents a traffic light detection system based on convolutional neural networks and lidar data. The proposed approach contains two stages. In the first detection stage, the map information is adopted to assist the object detection, and two cameras with different focal lengths are used to detect traffic lights at different distances. In the following recognition stage, we combine detector and classifier to deal with the problem of many light states. A dataset is created with urban road scenes in Taiwan. The experiments have demonstrated the advance in traffic light detection and recognition.

AB - This paper presents a traffic light detection system based on convolutional neural networks and lidar data. The proposed approach contains two stages. In the first detection stage, the map information is adopted to assist the object detection, and two cameras with different focal lengths are used to detect traffic lights at different distances. In the following recognition stage, we combine detector and classifier to deal with the problem of many light states. A dataset is created with urban road scenes in Taiwan. The experiments have demonstrated the advance in traffic light detection and recognition.

KW - ADAS

KW - state recognition

KW - traffic light detection

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U2 - 10.1109/ISPACS48206.2019.8986310

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BT - Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

Y2 - 3 December 2019 through 6 December 2019

ER -

Yeh TW, Lin SY, Lin HY, Chan SW, Lin CT, Lin YY. Traffic Light Detection using Convolutional Neural Networks and Lidar Data. In Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8986310. (Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019). doi: 10.1109/ISPACS48206.2019.8986310

Traffic Light Detection using Convolutional Neural Networks and Lidar Data

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