Abstract
Attention is the behavioral and cognitive process of selectively concentrating on a discrete aspect of information. It is the taking possession by the mind in clear and colorful form of one out of what seem several simultaneous objects or trains of thought. The purpose of this work is to establish a human–robot interaction system to detect the visual focus of attention (VFOA) based on human attention (in case of both reading and browsing purposes). The system detects the person’s current task (attention) and estimates the level by detecting the head and estimating eye region specially detected iris center within eye area (gaze pattern calculation). The system also determines the interest or willingness of the target person to interact with it based upon a certain level of VFOA. Then, depending on the level of interest of the target person, the system/robot generates awareness and establishes a communication channel with her/him.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Smith P, Shah M, da Vitoria Lobo N (2003) Determining driver visual attention with one camera. IEEE Trans Intell Transp Syst 4(4):205–218
Matsumoto Y, Ogasawara T, Zelinsky A (2000) Behavior recognition based on head-pose and gaze direction measurement. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, pp 2127–2132
Stiefelhagen R, Finke M, Yang J, Waibel A (1999) From gaze to focus of attention. In: Proceedings of the 3rd international conference on visual information systems, pp 761–768
Asteriadis S, Karpouzis K, Kollias SD (2011) Robust validation of visual focus of attention using adaptive fusion of head and eye gaze patterns. In: Proceedings of the IEEE international conference on computer vision workshops, pp 414–421
Asteriadis S, Karpouzis K, Kollias SD (2014) Visual focus of attention in non-calibrated environments using gaze estimation. Int J Comput Vis 107(3):293–316
Babcock JS, Pelz JB (2004) Building a lightweight eye tracking head-gear. In: Proceedings of the symposium on eye tracking research & applications, pp 109–114
Vertegaal R, Slagter R, van der Veer GC, Nijholt A (2001) Eye gaze patterns in conversations: there is more the conversational agents than meets the eyes. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 301–308
Ba SO, Hung H, Odobez J-M (2009) Visual activity context for focus of attention estimation in dynamic meetings. In: Proceedings of the IEEE international conference on multimedia and expo, June 28–July 2, pp 1424–1427
Voit M, Stiefelhagen R (2008) Deducing the visual focus of attention from head pose estimation in dynamic multi-view meeting scenarios. In: Proceedings of the 10th international conference on multimodal interfaces, 20–22 Oct, pp 173–180
Hoque MM, Deb K, Das D, Kobayashi Y, Kuno Y (2012) Attracting and controlling human attention through robot’s behaviors suited to the situation. In: Proceedings of the seventh annual ACM/IEEE international conference on human-robot interaction, pp 149–150, Mar 2012
Sheikhi S, Odobez J-M (2012) Recognizing the visual focus of attention for human robot interaction. HBU 2012: human behavior understanding, pp 99–112
Hoque MM, Deb K, Das D, Kobayashi Y, Kuno Y (2012) Design an intelligent robotic head to interacting with humans. In: 15th international conference on computer and information technology (ICCIT), Dec 2012
Onuki T, Ida K, Ezure T, Ishinoda T, Sano K, Kobayashi Y, Kuno Y (2014) Designing robot eyes and head and their motions for gaze communication. ICIC2014, LNCS8588, pp 607–618
Anzalone SM, Boucenna S, Ivaldi S, Chetouani M (2015) Evaluating the engagement with social robots. Int J Soc Robot 1–14
Afroze S, Hoque MM (2016) Detection of human’s focus of attention using head pose. ICAICT
Alam L, Hoque MM (2018) Vision-Based driver’s attention monitoring system for smart vehicles. In: ICO 2018: intelligent computing & optimization, pp 196–209, Sept 2018
Kendon A (1967) Some functions of gaze direction in social interaction. Aca Psychophys 25:22–63
Sidner CL, Lee C, Kidd CD, Rich C (2005) Explorations in engagement for humans and robots. Artif Intell 166:140–164
Mutlu B, Forlizzi J, Hodgins J (2006) A storytelling robot: modeling and evaluation of human-like gaze behavior. In: Proceedings of the HUMANOIDS’06, pp 518–523
Sonoyama T (2007) Introduction to robot design. Mainichi Commun (In Japanese)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chakraborty, P., Yousuf, M.A., Zahidur Rahman, M., Faruqui, N. (2020). How Can a Robot Calculate the Level of Visual Focus of Human’s Attention. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3607-6_27
Download citation
DOI: https://doi.org/10.1007/978-981-15-3607-6_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3606-9
Online ISBN: 978-981-15-3607-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)