Safe driving: driver action recognition using SURF keypoints

Imen Jegham; Anouar  Ben Khalifa; Ihsen Alouani; Mohamed Ali Mahjoub

doi:10.1109/icm.2018.8704009

Safe driving: driver action recognition using SURF keypoints

Imen Jegham, Anouar Ben Khalifa, Ihsen Alouani, Mohamed Ali Mahjoub

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

39 Citations (Scopus)

Abstract

Driver distraction is one of the main factors of fatal road traffic injuries. According to the national Highway Traffic Safety Administration (NHTSA), in USA, 3450 are killed by distracted driving, in 2016. In order to save lives, Advanced Driver Assistance Systems (ADAS), more specifically those systems for distracted driver action recognition are introduced. Our method aim to extract, from each frame, a region of interest (KOI) that contains body parts performing in-vehicle actions. These regions hold the most important key points after eliminating those common ones that are similar to the key points of the safe driving actions. The proposed approach was evaluated on the distracted driver detection dataset. Experimental results illustrate the performance of the proposed approach.

Original language	English
Title of host publication	Proceedings of the 30th International Conference on Microelectronics
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	60-63
Number of pages	4
ISBN (Electronic)	9781538681671
ISBN (Print)	9781538681688
DOIs	https://doi.org/10.1109/icm.2018.8704009
Publication status	Published - 02 May 2019
Externally published	Yes
Event	30th International Conference on Microelectronics - Sousse, Tunisia Duration: 16 Dec 2018 → 19 Dec 2018 https://doi.org/10.1109/ICM45233.2018

Publication series

Name	International Conference on Microelectronics: Proceedings
Publisher	IEEE

Conference

Conference	30th International Conference on Microelectronics
Abbreviated title	ICM
Country/Territory	Tunisia
City	Sousse
Period	16/12/2018 → 19/12/2018
Internet address	https://doi.org/10.1109/ICM45233.2018

Access to Document

10.1109/icm.2018.8704009Licence: Unspecified

Cite this

Jegham, I., Ben Khalifa, A., Alouani, I., & Mahjoub, M. A. (2019). Safe driving: driver action recognition using SURF keypoints. In Proceedings of the 30th International Conference on Microelectronics (pp. 60-63). (International Conference on Microelectronics: Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icm.2018.8704009

@inproceedings{4e5fe3cc7d08439abbcdd64b6a6fad5e,

title = "Safe driving: driver action recognition using SURF keypoints",

abstract = "Driver distraction is one of the main factors of fatal road traffic injuries. According to the national Highway Traffic Safety Administration (NHTSA), in USA, 3450 are killed by distracted driving, in 2016. In order to save lives, Advanced Driver Assistance Systems (ADAS), more specifically those systems for distracted driver action recognition are introduced. Our method aim to extract, from each frame, a region of interest (KOI) that contains body parts performing in-vehicle actions. These regions hold the most important key points after eliminating those common ones that are similar to the key points of the safe driving actions. The proposed approach was evaluated on the distracted driver detection dataset. Experimental results illustrate the performance of the proposed approach.",

author = "Imen Jegham and {Ben Khalifa}, Anouar and Ihsen Alouani and Mahjoub, {Mohamed Ali}",

year = "2019",

month = may,

day = "2",

doi = "10.1109/icm.2018.8704009",

language = "English",

isbn = "9781538681688",

series = "International Conference on Microelectronics: Proceedings",

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

pages = "60--63",

booktitle = "Proceedings of the 30th International Conference on Microelectronics",

address = "United States",

note = "30th International Conference on Microelectronics, ICM ; Conference date: 16-12-2018 Through 19-12-2018",

url = "https://doi.org/10.1109/ICM45233.2018",

}

Jegham, I, Ben Khalifa, A, Alouani, I & Mahjoub, MA 2019, Safe driving: driver action recognition using SURF keypoints. in Proceedings of the 30th International Conference on Microelectronics. International Conference on Microelectronics: Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 60-63, 30th International Conference on Microelectronics, Sousse, Tunisia, 16/12/2018. https://doi.org/10.1109/icm.2018.8704009

Safe driving: driver action recognition using SURF keypoints. / Jegham, Imen; Ben Khalifa, Anouar ; Alouani, Ihsen et al.
Proceedings of the 30th International Conference on Microelectronics. Institute of Electrical and Electronics Engineers Inc., 2019. p. 60-63 (International Conference on Microelectronics: Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Safe driving: driver action recognition using SURF keypoints

AU - Jegham, Imen

AU - Ben Khalifa, Anouar

AU - Alouani, Ihsen

AU - Mahjoub, Mohamed Ali

PY - 2019/5/2

Y1 - 2019/5/2

N2 - Driver distraction is one of the main factors of fatal road traffic injuries. According to the national Highway Traffic Safety Administration (NHTSA), in USA, 3450 are killed by distracted driving, in 2016. In order to save lives, Advanced Driver Assistance Systems (ADAS), more specifically those systems for distracted driver action recognition are introduced. Our method aim to extract, from each frame, a region of interest (KOI) that contains body parts performing in-vehicle actions. These regions hold the most important key points after eliminating those common ones that are similar to the key points of the safe driving actions. The proposed approach was evaluated on the distracted driver detection dataset. Experimental results illustrate the performance of the proposed approach.

AB - Driver distraction is one of the main factors of fatal road traffic injuries. According to the national Highway Traffic Safety Administration (NHTSA), in USA, 3450 are killed by distracted driving, in 2016. In order to save lives, Advanced Driver Assistance Systems (ADAS), more specifically those systems for distracted driver action recognition are introduced. Our method aim to extract, from each frame, a region of interest (KOI) that contains body parts performing in-vehicle actions. These regions hold the most important key points after eliminating those common ones that are similar to the key points of the safe driving actions. The proposed approach was evaluated on the distracted driver detection dataset. Experimental results illustrate the performance of the proposed approach.

U2 - 10.1109/icm.2018.8704009

DO - 10.1109/icm.2018.8704009

M3 - Conference contribution

SN - 9781538681688

T3 - International Conference on Microelectronics: Proceedings

SP - 60

EP - 63

BT - Proceedings of the 30th International Conference on Microelectronics

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 30th International Conference on Microelectronics

Y2 - 16 December 2018 through 19 December 2018

ER -

Safe driving: driver action recognition using SURF keypoints

Abstract

Publication series

Conference

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