计算机应用 ›› 2020, Vol. 40 ›› Issue (11): 3295-3299.DOI: 10.11772/j.issn.1001-9081.2020010008

• 虚拟现实与多媒体计算 • 上一篇    下一篇

基于头部姿态分析的摄像头视线追踪系统优化

赵昕晨, 杨楠   

  1. 中国人民大学 信息学院, 北京 100872
  • 收稿日期:2020-01-16 修回日期:2020-03-23 出版日期:2020-11-10 发布日期:2020-03-24
  • 通讯作者: 杨楠(1962-),男,辽宁辽阳人,副教授,博士,CCF会员,主要研究方向:数据挖掘、Web挖掘、机器学习;yangnan@ruc.edu.cn
  • 作者简介:赵昕晨(1995-),男,陕西渭南人,硕士研究生,主要研究方向:人机交互、视线追踪
  • 基金资助:
    中国人民大学科学研究基金(中央高校基本科研业务费专项资金资助)项目成果(20XNA031)。

Optimizing webcam-based eye tracking system via head pose analysis

ZHAO Xinchen, YANG Nan   

  1. School of Information, Renmin University of China, Beijing 100872, China
  • Received:2020-01-16 Revised:2020-03-23 Online:2020-11-10 Published:2020-03-24
  • Supported by:
    This work is partially supported by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (20XNA031).

摘要: 实时视线跟踪技术是智能眼动操作系统的关键技术。与基于眼动仪的技术相比,基于网络摄像头的技术具有低成本、高通用性等优点。针对现有的基于摄像头的算法只考虑眼部图像特征、准确度较低的问题,提出引入头部姿态分析的视线追踪算法优化技术。首先,通过人脸特征点检测结果构建头部姿态特征,为标定数据提供头部姿态上下文;然后,研究了新的相似度算法,计算头部姿态上下文的相似度;最后,在进行视线追踪时,利用头部姿态相似度对校准数据进行过滤,从标定数据集中选取与当前输入帧头部姿态相似度较高的数据进行预测。在选取不同特征人群的数据上进行了大量实验,对比实验结果显示,与WebGazer相比,所提算法的平均误差降低了58~63 px。所提算法能有效提高追踪结果的准确性和稳定性,拓展了摄像头设备在视线追踪领域的应用场景。

关键词: 视线追踪, 网络摄像头, 头部姿态上下文, 人脸特征点检测, 头部姿态相似度

Abstract: Real-time eye tracking technology is the key technology of intelligent eye movement operating system. Compared to the technology based on eye tracker, the technology based on webcam has the advantages of low cost and high universality. Aiming at the low accuracy problem of the existing webcam based algorithms only with the eye image features considered, an optimization technology for eye tracking algorithm with head pose analysis introduced was proposed. Firstly, the head pose features were constructed based on the results of facial feature point tracking to provide head pose context for the calibration data. Secondly, a new similarity algorithm was studied to calculate the similarity of the head pose context. Finally, during the eye tracking, the head pose similarity was used to filter the calibration data, and the data with higher head pose similarity to the current input frame was selected from the calibration dataset for prediction. A large number of experiments were carried out on the data of populations with different characteristics. The comparison experimental results show that compared with WebGazer, the proposed algorithm has the average error reduced by 58 to 63 px. The proposed algorithm can effectively improve the accuracy and stability of the tracking results, and expand the application scenarios of webcam in the field of eye tracking.

Key words: eye tracking, webcam, head pose context, facial feature detection, head pose similarity

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