@inproceedings{fbfa76cc498740c3b275a156b582d0e4,
title = "Cluster based gaze estimation and data visualization supporting diverse environments",
abstract = "This paper proposes a method to explore the navigation of audience by estimating gaze points and labeling them to the objects of video or web content. The cluster based gaze estimation and statistics based labeling method are proposed to support the multiple user environments as well as improve the accuracy and usability. In addition, the number of feature clusters is accorded as the amount of areas of target screen. Additionally, statistics based labeling method is proposed to mapping the gaze points to target object by computing the probability. Moreover, this method can provide a good manner to estimate the attentive objects. The visualization of attentive blocks and the amount of probabilities are presented to illustrate the navigation of audience as attentive object. Therefore, this visualization method can exhibit the navigation behavior of audience more real, besides overcomes the problem of difficult labeling objects. The experimental results demonstrate the proposed method provide more robustness for long range as well as diversity environments.",
keywords = "Cluster, Data, Gaze Estimation, Statistics Based Labeling",
author = "Kao, {Chiao Wen} and Hwang, {Bor Jiunn} and Chen, {Hui Hui} and Fan, {Kuo Chin} and Wu, {Shyi Huey}",
note = "Publisher Copyright: {\textcopyright} 2017 Association for Computing Machinery.; 2017 International Conference on Watermarking and Image Processing, ICWIP 2017 ; Conference date: 06-09-2017 Through 08-09-2017",
year = "2017",
month = sep,
day = "6",
doi = "10.1145/3150978.3150988",
language = "???core.languages.en_GB???",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "37--41",
booktitle = "ICWIP 2017 - 2017 International Conference on Watermarking and Image Processing",
}