A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicated Performance of Virtual Driving

Zezhong Lv; Qing Xu; Klaus Schoeffmann; Simon Parkinson

doi:10.1109/ICME51207.2021.9428109

A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicated Performance of Virtual Driving

Zezhong Lv, Qing Xu, Klaus Schoeffmann, Simon Parkinson

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

2 Citations (Scopus)

Abstract

Visual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as driving. In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks in 3D dynamic environments. We exploit the use of eye tracking data as a behaviometric, for indicating the visuo-motor behavioral measure in the context of virtual driving. A new metric of visual scanning efficiency (VSE), which is defined as a mathematical divergence between a fixation distribution and a distribution of optical flows induced by fixations, is proposed by making use of a widely known information theoretic tool, namely the square root of Jensen-Shannon divergence. Psychophysical eye tracking studies, in virtual reality based driving, are conducted to reveal that the new metric of visual scanning efficiency can be employed very well as a proxy evaluation for driving performance. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activities.

Original language	English
Title of host publication	IEEE International Conference on Multimedia and Expo
Subtitle of host publication	ICME 2021
Publisher	IEEE Computer Society
Number of pages	6
ISBN (Electronic)	9781665438643
ISBN (Print)	9781665411523
DOIs	https://doi.org/10.1109/ICME51207.2021.9428109
Publication status	Published - 9 Jun 2021
Event	2021 IEEE International Conference on Multimedia and Expo - Virtual event due to COVID-19, Virtual event, China Duration: 5 Jul 2021 → 9 Jul 2021 https://2021.ieeeicme.org/

Publication series

Name	Proceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)	1945-7871
ISSN (Electronic)	1945-788X

Conference

Conference	2021 IEEE International Conference on Multimedia and Expo
Abbreviated title	ICME 2021
Country/Territory	China
City	Virtual event
Period	5/07/21 → 9/07/21
Internet address	https://2021.ieeeicme.org/

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Lv, Z., Xu, Q., Schoeffmann, K., & Parkinson, S. (2021). A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicated Performance of Virtual Driving. In IEEE International Conference on Multimedia and Expo: ICME 2021 [9428109] (Proceedings - IEEE International Conference on Multimedia and Expo). IEEE Computer Society. https://doi.org/10.1109/ICME51207.2021.9428109

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title = "A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicated Performance of Virtual Driving",

abstract = "Visual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as driving. In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks in 3D dynamic environments. We exploit the use of eye tracking data as a behaviometric, for indicating the visuo-motor behavioral measure in the context of virtual driving. A new metric of visual scanning efficiency (VSE), which is defined as a mathematical divergence between a fixation distribution and a distribution of optical flows induced by fixations, is proposed by making use of a widely known information theoretic tool, namely the square root of Jensen-Shannon divergence. Psychophysical eye tracking studies, in virtual reality based driving, are conducted to reveal that the new metric of visual scanning efficiency can be employed very well as a proxy evaluation for driving performance. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activities.",

keywords = "behaviometric, eye tracking, Jensen-Shannon divergence (JSD), visual scanning efficiency",

author = "Zezhong Lv and Qing Xu and Klaus Schoeffmann and Simon Parkinson",

note = "Funding Information: Qing Xu is corresponding author. This work is funded by Natural Science Foundation of China under Grants No. 61471261 and No. 61771335, and is funded by The National Key R&D Program of China (Grant No. 2020YFC1807904 and No. 2020YFC1807905). Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Conference on Multimedia and Expo, ICME 2021 ; Conference date: 05-07-2021 Through 09-07-2021",

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doi = "10.1109/ICME51207.2021.9428109",

language = "English",

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Lv, Z, Xu, Q, Schoeffmann, K & Parkinson, S 2021, A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicated Performance of Virtual Driving. in IEEE International Conference on Multimedia and Expo: ICME 2021., 9428109, Proceedings - IEEE International Conference on Multimedia and Expo, IEEE Computer Society, 2021 IEEE International Conference on Multimedia and Expo, Virtual event, China, 5/07/21. https://doi.org/10.1109/ICME51207.2021.9428109

A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicated Performance of Virtual Driving. / Lv, Zezhong; Xu, Qing; Schoeffmann, Klaus et al.

IEEE International Conference on Multimedia and Expo: ICME 2021. IEEE Computer Society, 2021. 9428109 (Proceedings - IEEE International Conference on Multimedia and Expo).

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

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AU - Lv, Zezhong

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AU - Schoeffmann, Klaus

AU - Parkinson, Simon

N1 - Funding Information: Qing Xu is corresponding author. This work is funded by Natural Science Foundation of China under Grants No. 61471261 and No. 61771335, and is funded by The National Key R&D Program of China (Grant No. 2020YFC1807904 and No. 2020YFC1807905). Publisher Copyright: © 2021 IEEE

PY - 2021/6/9

Y1 - 2021/6/9

N2 - Visual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as driving. In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks in 3D dynamic environments. We exploit the use of eye tracking data as a behaviometric, for indicating the visuo-motor behavioral measure in the context of virtual driving. A new metric of visual scanning efficiency (VSE), which is defined as a mathematical divergence between a fixation distribution and a distribution of optical flows induced by fixations, is proposed by making use of a widely known information theoretic tool, namely the square root of Jensen-Shannon divergence. Psychophysical eye tracking studies, in virtual reality based driving, are conducted to reveal that the new metric of visual scanning efficiency can be employed very well as a proxy evaluation for driving performance. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activities.

AB - Visual scanning plays an important role in sampling visual information from the surrounding environments for a lot of everyday sensorimotor tasks, such as driving. In this paper, we consider the problem of visual scanning mechanism underpinning sensorimotor tasks in 3D dynamic environments. We exploit the use of eye tracking data as a behaviometric, for indicating the visuo-motor behavioral measure in the context of virtual driving. A new metric of visual scanning efficiency (VSE), which is defined as a mathematical divergence between a fixation distribution and a distribution of optical flows induced by fixations, is proposed by making use of a widely known information theoretic tool, namely the square root of Jensen-Shannon divergence. Psychophysical eye tracking studies, in virtual reality based driving, are conducted to reveal that the new metric of visual scanning efficiency can be employed very well as a proxy evaluation for driving performance. These results suggest that the exploitation of eye tracking data provides an effective behaviometric for sensorimotor activities.

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DO - 10.1109/ICME51207.2021.9428109

M3 - Conference contribution

AN - SCOPUS:85126448121

SN - 9781665411523

T3 - Proceedings - IEEE International Conference on Multimedia and Expo

BT - IEEE International Conference on Multimedia and Expo

PB - IEEE Computer Society

T2 - 2021 IEEE International Conference on Multimedia and Expo

Y2 - 5 July 2021 through 9 July 2021

ER -

A Jensen-Shannon Divergence Driven Metric of Visual Scanning Efficiency Indicated Performance of Virtual Driving

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