Resumé
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 | |
| 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 | USA |
| By | Honolulu |
| Periode | 21/07/2017 → 26/07/2017 |
| Navn | IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
|---|---|
| ISSN | 2160-7516 |
Fingerprint
Citer dette
}
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
TY - GEN
T1 - Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data
AU - Jensen, Morten Bornø
AU - Nasrollahi, Kamal
AU - Moeslund, Thomas B.
PY - 2017/7/21
Y1 - 2017/7/21
N2 - 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 %.
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 %.
U2 - 10.1109/CVPRW.2017.122
DO - 10.1109/CVPRW.2017.122
M3 - Article in proceeding
SN - 978-1-5386-0734-3
T3 - IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
BT - 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops
PB - IEEE
ER -