Object-based Visual Attention Quantification using Head Orientation in VR Applications

doi:10.23940/ijpe.19.03.p2.732742

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (3): 732-742.doi: 10.23940/ijpe.19.03.p2.732742

Previous Articles Next Articles

Object-based Visual Attention Quantification using Head Orientation in VR Applications

Honglei Han^{a, b, *}, Aidong Lu^b, Chanchan Xu^a, and Unique Wells^b

a School of Animation and Digital Arts, Communication University of China, Beijing, 100024, China;
b Department of Computer Science, University of North Carolina at Charlotte, Charlotte, 28223, USA

Submitted on ; Revised on ;
Contact: hanhonglei@cuc.edu.cn
About author:Honglei Han joined the Communication University of China after receiving his M.S. degree in computer science in 2006. He is currently an associate professor. He received his Ph.D. from the Institute of Software at Chinese Academy of Sciences in 2015. He was a visiting scholar at the University of North Carolina at Charlotte. His research interests include computer graphics, virtual reality, and computer games.Aidong Lu received her bachelor's and Master's degrees in computer science from Tsinghua University in 1999 and 2001, respectively, and her Ph.D. in electrical and computer engineering from Purdue University in 2005. She is currently an associate professor at the University of North Carolina at Charlotte. Her research interest is developing effective visualization approaches to improve visual communications in real-life applications and education. Chanchan Xu obtained her Master's degree in computer application technology from Beijing Forestry University in 2013. She is currently a graduate student at the Communication University of China. Her research interests are computer graphics and virtual reality.Unique Wells is a Master's degree candidate at the University of North Carolina at Charlotte. Her research interests are data visualization and virtual reality.

Abstract

Abstract: This paper presents a method to measure what and how deep users can perceive when exploring virtual reality environments using a head mounted display. A preliminary user study was conducted to verify that user gaze behavior has specific differences in immersive virtual reality environments compared with that in conventional, non-immersive virtual reality environments, which are based on a desktop screen. Gathered from the study results for gaze behavior, the users experiencing immersive virtual reality environments are more likely to adjust their head movement to center interesting objects in their vision. Based on this finding, a quantitative method is proposed to measure the user's visual attention in such a virtual reality environment. A user personalized storyboard is designed to capture the user's most regarded views as key frames that can depict the users' exploration experience in immersive virtual reality environments.

Key words: gaze analysis, visual attention, virtual reality, interaction, eye tracking

Honglei Han, Aidong Lu, Chanchan Xu, and Unique Wells. Object-based Visual Attention Quantification using Head Orientation in VR Applications [J]. Int J Performability Eng, 2019, 15(3): 732-742.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. M. Sivak, “The Information that Drivers Use: Is it Indeed 90% Visual?” Perception, Vol. 25, No. 9, pp. 1081-1089, 1996
2. K. Rayner, “Eye Movements and Attention in Reading, Scene Perception, and Visual Search,” The Quarterly Journal of Experimental Psychology, Vol. 62, No. 8, pp. 1457-1506, 2009
3. A. Mack and I. Rock, “Inattentional Blindness,” MIT Press, MA, 1998
4. D. J.Simons and C. F. Chabris, “Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events,” Perception, Vol. 28, No. 9, pp. 1059-1074, 1999
5. R. J.Peters and L. Itti, “Applying Computational Tools to Predict Gaze Direction in Interactive Visual Environments,” ACM Transactions on Applied Perception, Vol. 5, No. 2, pp. 1-19, 2008
6. A. T. Duchowski, “Eye Tracking Methodology: Theory and Practice,” Springer-Verlag, London, 2007
7. M. Bernhard, E. Stavrakis,M. Wimmer, “An Empirical Pipeline to Derive Gaze Prediction Heuristics for 3D Action Games,” ACM Transactions on Applied Perception (TAP), Vol. 8, No. 1, 2010
8. “Tobii Eye Tracking in Virtual Reality,”(https://vr.tobii.com/, Last accessed on October 23, 2018)
9. R. J. Snowden, P. Thompson,T. Troscianko, “Basic Vision : An Introduction to Visual Perception,” Oxford University Press, Oxford, 2012
10. S. Hillaire, A. Lécuyer, T. Regiacorte, R. Cozot, J. Royan,G. Breton, “Design and Application of Real-Time Visual Attention Model for the Exploration of 3D Virtual Environments,” IEEE Transactions on Visualization & Computer Graphics, Vol. 18, No. 3, pp. 356-368, 2012
11. L. Itti, “Quantifying the Contribution of Low-Level Saliency to Human Eye Movements in Dynamic Scenes,” Visual Cognition, Vol. 12, No. 6, pp. 1093-1123, 2005
12. S. Lee, G. J. Kim,S. Choi, “Real-Time Tracking of Visually Attended Objects in Virtual Environments and its Application to Lod,” IEEE Transactions on Visualization & Computer Graphics, Vol. 15, No. 1, pp. 6-19, 2009
13. K. Yun, Y. Peng, D. Samaras, G. J. Zelinsky,T. L. Berg, “Studying Relationships between Human Gaze, Description, and Computer Vision,” in Proceedings of Computer Vision and Pattern Recognition, pp. 739-746, 2013
14. V. Sitzmann, A. Serrano, A. Pavel, M. Agrawala, D. Gutierrez,G. Wetzstein, “Saliency in VR: How Do People Explore Virtual Environments?” IEEE Transactions on Visualization and Computer Graphics, Vol. 24, No. 4, pp. 1633-1642, 2018
15. V. Sundstedt, M. Bernhard, E. Stavrakis, E. Reinhard,M. Wimmer, “Visual Attention and Gaze Behavior in Games: An Object-based Approach,” Springer-Verlag, London, 2013
16. M. Land, N. Mennie,J. Rusted, “The Roles of Vision and Eye Movements in the Control of Activities of Daily Living,” Perception, Vol. 28, No. 11, pp. 1311-1328, 1999
17. G. N. Yannakakis, H. P. Martínez,A. Jhala, “Towards Affective Camera Control in Games,” User Modeling and User-Adapted Interaction, Vol. 20, No. 4, pp. 313-340, 2010
18. P. Burelli, “Virtual Cinematography in Games: Investigating the Impact on Player Experience,” in Proceedings of International Conference on the Foundations of Digital Games Society for the Advancement of the Science of Digital Games, pp. 134-141, 2013
19. P. Majaranta and A. Bulling, “Eye Tracking and Eye-based Human-Computer Interaction,” Springer-Verlag, London, 2014
20. T. Nakayama, H. Kato,Y. Yamane, “Discovering the Gap between Web Site Designers' Expectations and Users' Behavior,” Computer Networks, Vol. 33, No.1-6, pp. 811-822, 2000
21. F. Alt, A. S. Shirazi, A. Schmidt,J. Mennen, “Increasing the User's Attention on the Web: Using Implicit Interaction based on Gaze Behavior to Tailor Content,” inProceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design, pp. 544-553, Copenhagen, Denmark, 2012
22. T. Löwe, M. Stengel, E. C. Förster, S. Grogorick,M. Magnor, “Visualization and Analysis of Head Movement and Gaze Data for Immersive Video in Head-Mounted Displays,” in Proceedings of Workshop on Eye Tracking and Visualization, pp. 141-146, 2015
23. A. Picardi, P. Burelli,G. N. Yannakakis, “Modelling Virtual Camera Behaviour through Player Gaze,” inProceedings of the 6^th International Conference on Foundations of Digital Games, pp. 107-114, 2011
24. P. Burelli and G. N. Yannakakis, “Towards Adaptive Virtual Camera Control in Computer Games,” in Proceedings of International Symposium on Smart Graphics, pp. 25-36, Berlin, Heidelberg, 2011
25. Y. F. Ma, X. S. Hua, L. Lu,H. J. Zhan, “A Generic Framework of User Attention Model and Its Application in Video Summarization,” IEEE Transactions on Multimedia, Vol. 7, No. 5, pp. 907-919, 2005
26. M. Slater, “Place Illusion and Plausibility Can Lead to Realistic Behaviour in Immersive Virtual Environments,” Philosophical Transactions of the Royal Society of London, Vol. 364, No. 1535, pp. 3549-3557, 2009
27. C. Papadopoulos, I. Gutenko,A. E. Kaufman, “Veevvie: Visual Explorer for Empirical Visualization, VR and Interaction Experiments,” IEEE Transactions on Visualization & Computer Graphics, Vol. 22, No. 1, pp. 111-120, 2016
28. A. Steed, S. Friston, M. Lopez, J. Drummond, Y. Pan,D. Swapp, “An 'in the Wild' Experiment on Presence and Embodiment using Consumer Virtual Reality Equipment,” IEEE Transactions on Visualization & Computer Graphics, Vol. 22, No. 4, pp. 1406-1414, 2016

[1]	Amanpreet Kaur and Archana Mantri. Evaluating the Impact of Hybridization of Vision and Sensor-Based Tracking on the Accuracy and Robustness of Virtual Reality-Based Shooting Tutor for Defense Training [J]. Int J Performability Eng, 2023, 19(9): 559-567.
[2]	Amanpreet Kaur, Alisa Gazizullina, and Archana Mantri. Analyzing the Effects of Virtual Reality Headsets on Human Memory [J]. Int J Performability Eng, 2023, 19(4): 234-241.
[3]	Shaorya Raj, Tuhin Kalia, Shivansh Aggarwal, Sahil Jaglan, Nikita Nijhawan, and Mugdha Sharma. Human Computer Interaction using Virtual User Computer Interaction System [J]. Int J Performability Eng, 2022, 18(6): 396-406.
[4]	Pawan Wawage and Yogesh Deshpande. Real-Time Prediction of Car Driver’s Emotions using Facial Expression with a Convolutional Neural Network-based Intelligent System [J]. Int J Performability Eng, 2022, 18(11): 791-797.
[5]	Zhaoyu Shou, Yiru Wen, Pan Chen, and Huibing Zhang. Personalized Knowledge Map Recommendations based on Interactive Behavior Preferences [J]. Int J Performability Eng, 2021, 17(1): 36-49.
[6]	Zhaoyu Shou, Yanguo Wang, Yiru Wen, and Huibing Zhang. Knowledge Point Recommendation Algorithm based on Enhanced Correction Factor and Weighted Sequential Pattern Mining [J]. Int J Performability Eng, 2020, 16(4): 549-559.
[7]	Hui Liang, Chao Ge, Fei Liang, and Yusheng Sun. VR-based Training Model for Enhancing Fire Evacuee Safety [J]. Int J Performability Eng, 2020, 16(1): 107-117.
[8]	Junbo Wang, Ming Yang, and Yuxin Zhang. Detector Layout and Detection Probability Analysis for Physical Protection Systems of Nuclear Power Plants in Virtual Environments [J]. Int J Performability Eng, 2019, 15(4): 1255-1262.
[9]	Mingyang Sun and Haiyang Yu. Automobile Intelligent Dashboard Design based on Human Computer Interaction [J]. Int J Performability Eng, 2019, 15(2): 571-578.
[10]	Yi Wang. Virtual or Real: Discussion on Application of Semi-Virtual Exhibition Space [J]. Int J Performability Eng, 2019, 15(2): 630-636.
[11]	Yuan Zhong, Jiqin Wu, Feng Han, and Jiawei Zhang. A Model for Pantograph-Catenary Electromechanical Interaction [J]. Int J Performability Eng, 2018, 14(12): 3140-3150.
[12]	Rui Han, Zhiquan Feng, Changsheng Ai, Wei Xie, and Kang Wang. A Data Glove-based KEM Dynamic Gesture Recognition Algorithm [J]. Int J Performability Eng, 2018, 14(11): 2590-2600.

Object-based Visual Attention Quantification using Head Orientation in VR Applications

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 12

Recommended 0