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

資訊工程系

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

14 引文斯高帕斯（Scopus）

摘要

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.

原文	English
主出版物標題	Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781728130385
DOIs	https://doi.org/10.1109/ISPACS48206.2019.8986310
出版狀態	Published - 12月 2019
事件	2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 - Taipei, Taiwan 持續時間: 3 12月 2019 → 6 12月 2019

出版系列

名字	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
國家/地區	Taiwan
城市	Taipei
期間	3/12/19 → 6/12/19

文獻附註

Publisher Copyright:
© 2019 IEEE.

存取文件

10.1109/ISPACS48206.2019.8986310

其它文件與鏈接

Link to publication in Scopus

引用此

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. 於 Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019 文章 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 等. / 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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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

}

Yeh, TW, Lin, SY, Lin, HY, Chan, SW, Lin, CT & Lin, YY 2019, Traffic Light Detection using Convolutional Neural Networks and Lidar Data. 於 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 等.
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).

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

TY - GEN

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

AU - Yeh, Tien Wen

AU - Lin, Ssu Yun

AU - Lin, Huei Yung

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

UR - https://www.scopus.com/pages/publications/85081091197

U2 - 10.1109/ISPACS48206.2019.8986310

DO - 10.1109/ISPACS48206.2019.8986310

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:85081091197

T3 - Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019

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. 於 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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