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Gaze behavior data profiling and analysis system platform based on visual content representation

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Abstract

This paper presents a real-time system platform for profiling and analyzing the gaze behavior based on visual contents. The proposed system captures the gaze information from multiple users and provides the ability to measure the degree of visual content perception by the users through statistical analysis. Visual content representation scheme is presented for capturing and annotating the gaze behavior effectively. Information correlation property among multiple image frames is defined for providing the ability to analyze the pattern of perception of user based on complex visual contents. In order to monitor the real time gaze behavior of the users, the monitoring rules are incorporated in to the representation template. The number of users and the duration of observation time may significantly increase the profile data size. To alleviate the problem, an adaptive data compression techniques is incorporated. The capabilities and functionalities of the proposed system are verified for multiple users with different gaze behavior on a sequence of commercial image data.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, 11794, USA

    Jung-Min Oh & Sangjin Hong

  2. Department of Computer Engineering, Hoseo University, Asan, 336-795, South Korea

    Nammee Moon

Authors
  1. Jung-Min Oh
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  2. Sangjin Hong
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  3. Nammee Moon
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Corresponding author

Correspondence to Nammee Moon.

Additional information

This research was supported by the ‘Cross-Ministry Giga KOREA Project’ of the Ministry of Science, ICT and Future Planning, Republic of Korea (ROK).[GK14P0100, Development of Tele-Experience Service SW Platform based on Giga Media]. Also, this paper is extended and improved from accepted paper of KCIC-2013/FCC-2014 conferences.

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Oh, JM., Hong, S. & Moon, N. Gaze behavior data profiling and analysis system platform based on visual content representation. Multimed Tools Appl 75, 15211–15227 (2016). https://doi.org/10.1007/s11042-014-2285-7

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  • DOI: https://doi.org/10.1007/s11042-014-2285-7

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