Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data

Morten Bornø Jensen; Kamal Nasrollahi; Thomas B. Moeslund

doi:10.1109/CVPRW.2017.122

Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data

Morten Bornø Jensen, Kamal Nasrollahi, Thomas B. Moeslund

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

65 Citationer (Scopus)

224 Downloads (Pure)

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 %.

Originalsprog	Engelsk
Titel	2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops : Traffic Surveillance Workshop and Challenge
Forlag	IEEE
Publikationsdato	21 jul. 2017
ISBN (Trykt)	978-1-5386-0734-3
ISBN (Elektronisk)	978-1-5386-0733-6
DOI	https://doi.org/10.1109/CVPRW.2017.122
Status	Udgivet - 21 jul. 2017
Begivenhed	2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge - Hawaii Convention Center, Honolulu, USA Varighed: 21 jul. 2017 → 26 jul. 2017

Konference

Konference	2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge
Lokation	Hawaii Convention Center
Land/Område	USA
By	Honolulu
Periode	21/07/2017 → 26/07/2017

Navn	IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISSN	2160-7516

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10.1109/CVPRW.2017.122

Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paperAccepteret manuskript, 0,98 MB

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Citationsformater

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title = "Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data",

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 %.",

author = "Jensen, {Morten Born{\o}} and Kamal Nasrollahi and Moeslund, {Thomas B.}",

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series = "IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)",

publisher = "IEEE",

booktitle = "2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops",

address = "United States",

note = "2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge ; Conference date: 21-07-2017 Through 26-07-2017",

}

Jensen, MB, Nasrollahi, K & Moeslund, TB 2017, Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data. i 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), 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops: Traffic Surveillance Workshop and Challenge, Honolulu, Hawaii, USA, 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)).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer 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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Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data

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