Comprehensive Parameter Sweep for Learning-based Detector on Traffic Lights

Morten Bornø Jensen; Mark Philip Philipsen; Mohan M. Trivedi; Thomas B. Moeslund

doi:10.1007/978-3-319-50832-0_10

Comprehensive Parameter Sweep for Learning-based Detector on Traffic Lights

Morten Bornø Jensen, Mark Philip Philipsen, Mohan M. Trivedi, Thomas B. Moeslund

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

1 Citationer (Scopus)

417 Downloads (Pure)

Abstract

Determining the optimal parameters for a given detection algorithm is not straightforward and what ends up as the final values is mostly based on experience and heuristics. In this paper we investigate the influence of three basic parameters in the widely used Aggregate Channel Features (ACF) object detector applied for traffic light detec- tion. Additionally, we perform an exhaustive search for the optimal pa- rameters for the night time data from the LISA Traffic Light Dataset. The optimized detector reaches an Area-Under-Curve of 66.63 % on cal- culated precision-recall curve.

Originalsprog	Engelsk
Titel	Advances in Visual Computing. ISVC 2016 : Lecture Notes in Computer Science (LNCS)
Vol/bind	10073
Forlag	Springer
Publikationsdato	10 dec. 2016
Sider	92-100
DOI	https://doi.org/10.1007/978-3-319-50832-0_10
Status	Udgivet - 10 dec. 2016
Begivenhed	ISVC16: 12th International Symposium on Visual Computing - Monte Carlo Resort & Casino, Las Vegas, USA Varighed: 12 dec. 2016 → 14 dec. 2016 http://www.isvc.net

Konference

Konference	ISVC16: 12th International Symposium on Visual Computing
Lokation	Monte Carlo Resort & Casino
Land/Område	USA
By	Las Vegas
Periode	12/12/2016 → 14/12/2016
Internetadresse	http://www.isvc.net

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10.1007/978-3-319-50832-0_10

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Citationsformater

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title = "Comprehensive Parameter Sweep for Learning-based Detector on Traffic Lights",

abstract = "Determining the optimal parameters for a given detection algorithm is not straightforward and what ends up as the final values is mostly based on experience and heuristics. In this paper we investigate the influence of three basic parameters in the widely used Aggregate Channel Features (ACF) object detector applied for traffic light detec- tion. Additionally, we perform an exhaustive search for the optimal pa- rameters for the night time data from the LISA Traffic Light Dataset. The optimized detector reaches an Area-Under-Curve of 66.63 % on cal- culated precision-recall curve.",

author = "Jensen, {Morten Born{\o}} and Philipsen, {Mark Philip} and Trivedi, {Mohan M.} and Moeslund, {Thomas B.}",

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Jensen, MB, Philipsen, MP, Trivedi, MM & Moeslund, TB 2016, Comprehensive Parameter Sweep for Learning-based Detector on Traffic Lights. i Advances in Visual Computing. ISVC 2016: Lecture Notes in Computer Science (LNCS). bind 10073, Springer, s. 92-100, ISVC16: 12th International Symposium on Visual Computing, Las Vegas, USA, 12/12/2016. https://doi.org/10.1007/978-3-319-50832-0_10

Comprehensive Parameter Sweep for Learning-based Detector on Traffic Lights. / Jensen, Morten Bornø; Philipsen, Mark Philip; Trivedi, Mohan M. et al.
Advances in Visual Computing. ISVC 2016: Lecture Notes in Computer Science (LNCS). Bind 10073 Springer, 2016. s. 92-100.

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

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AB - Determining the optimal parameters for a given detection algorithm is not straightforward and what ends up as the final values is mostly based on experience and heuristics. In this paper we investigate the influence of three basic parameters in the widely used Aggregate Channel Features (ACF) object detector applied for traffic light detec- tion. Additionally, we perform an exhaustive search for the optimal pa- rameters for the night time data from the LISA Traffic Light Dataset. The optimized detector reaches an Area-Under-Curve of 66.63 % on cal- culated precision-recall curve.

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Comprehensive Parameter Sweep for Learning-based Detector on Traffic Lights

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