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Abstract

Traffic light detection (TLD) is a vital part of both intel- ligent vehicles and driving assistance systems (DAS). Gen- eral for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact per- formance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions.
The YOLO object detector achieves an AUC of impres- sively 90.49 % for daysequence1, which is an improvement of 50.32 % compared to the latest ACF entry in the VIVA- challenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3 %, which is in an increase of 18.13 %.
Original languageEnglish
Title of host publication2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops : Traffic Surveillance Workshop and Challenge
PublisherIEEE
Publication date21 Jul 2017
ISBN (Print)978-1-5386-0734-3
ISBN (Electronic)978-1-5386-0733-6
DOIs
Publication statusPublished - 21 Jul 2017
Event2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge - Hawaii Convention Center, Honolulu, United States
Duration: 21 Jul 201726 Jul 2017

Conference

Conference2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge
LocationHawaii Convention Center
CountryUnited States
CityHonolulu
Period21/07/201726/07/2017
SeriesIEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISSN2160-7516

Fingerprint

Telecommunication traffic
Detectors
Intelligent vehicle highway systems

Cite this

Jensen, M. B., Nasrollahi, K., & Moeslund, T. B. (2017). Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge IEEE. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) https://doi.org/10.1109/CVPRW.2017.122
Jensen, Morten Bornø ; Nasrollahi, Kamal ; Moeslund, Thomas B. / Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge. IEEE, 2017. (IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)).
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abstract = "Traffic light detection (TLD) is a vital part of both intel- ligent vehicles and driving assistance systems (DAS). Gen- eral for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact per- formance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions.The YOLO object detector achieves an AUC of impres- sively 90.49 {\%} for daysequence1, which is an improvement of 50.32 {\%} compared to the latest ACF entry in the VIVA- challenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3 {\%}, which is in an increase of 18.13 {\%}.",
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Jensen, MB, Nasrollahi, K & Moeslund, TB 2017, Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data. in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge. IEEE, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, United States, 21/07/2017. https://doi.org/10.1109/CVPRW.2017.122

Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data. / Jensen, Morten Bornø; Nasrollahi, Kamal; Moeslund, Thomas B.

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge. IEEE, 2017. (IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AB - Traffic light detection (TLD) is a vital part of both intel- ligent vehicles and driving assistance systems (DAS). Gen- eral for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact per- formance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions.The YOLO object detector achieves an AUC of impres- sively 90.49 % for daysequence1, which is an improvement of 50.32 % compared to the latest ACF entry in the VIVA- challenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3 %, which is in an increase of 18.13 %.

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Jensen MB, Nasrollahi K, Moeslund TB. Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge. IEEE. 2017. (IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)). https://doi.org/10.1109/CVPRW.2017.122