动态贝叶斯网络的立体视觉疲劳概率评估

吕立程; 桑胜波

doi:doi:10.3969/j.issn.1671-637x.2019.09.011

电光与控制, 2019, 26 (9): 45, 网络出版: 2021-01-31

动态贝叶斯网络的立体视觉疲劳概率评估

Stereoscopic Fatigue Probability Assessment of Dynamic Bayesian NetworksL

论文大纲

吕立程桑胜波

作者单位

太原理工大学, 山西晋中 030600

摘要

为了更有效地评估3D设备观测导致的视觉疲劳, 首次采用动态贝叶斯网络对3D观测者的立体视觉疲劳概率进行计算。在构建有向无环图的过程中考虑立体视觉中的多种因素与疲劳现象之间的相互关系, 在疲劳节点上加入生理特征节点与动态因素进行合理评估, 使得各节点的状态与贝叶斯网络节点的状态概率一一对应, 为立体视觉疲劳概率评估提供了系统的方案。结果表明, 与当前MOS方案相比, 采用动态贝叶斯网络的方案更为全面地分析了观测者的疲劳状态, 所评估的疲劳概率比观测者的主观结果更精确, 更为接近实际情况。

Abstract

In order to evaluate the visual fatigue caused by 3D equipment observation more effectively, the dynamic Bayesian network is used to calculate the stereoscopic visual fatigue probability of 3D observers for the first time. In the process of constructing a directed acyclic graph, the relationship between multiple factors in stereo vision and fatigue phenomena is taken into accountthe physiological characteristic nodes and dynamic factors are added for making reasonable estimation. The state of each node is in one-to-one corres- pondence with the state probability of Bayesian network nodes, which provides a systematic scheme for stereoscopic fatigue probability assessment. The results show that: Compared with current MOS method, the scheme of dynamic Bayesian network analyzes the observation more comprehensively, the fatigue state of the observer is more accurate than the subjective result of the observer himself, and is closer to the actual situation.

参考文献

[1] 靳冰凌, 张震, 张子耀. 基于视觉的驾驶员疲劳特征提取方法［J］. 计算机技术与发展, 2018, 28(11): 193-197.

[2] 王静. 基于功能磁共振的立体视觉功能区定位及观看3D电视视疲劳研究［D］. 南京: 南京航空航天大学,2016.

[3] 沈丽丽, 孙伟鹏. 立体深度运动引发的立体视觉疲劳的脑电评估［J］. 工程科学学报, 2017, 39(9): 1421-1427.

[4] 李静, 王阿妮, 王君乐. 三维显示技术引起的视觉疲劳研究综述［J］. 激光与光电子学进展, 2015, 52(3): 74-83.

[5] 王嘉辉, 程义, 李焜阳, 等. 3D显示方式与视差对视觉疲劳的影响研究［J］. 中山大学学报: 自然科学版, 2013, 52(5): 1-5.

[6] 施笑畏, 王帅. 基于眼球运动参数视觉疲劳评价［J］. 科技信息, 2012(21): 172-173.

[7] 映维网. 伊利诺伊大学为VR研发3D显示屏,解决视觉疲劳问题［J］. 工业设计, 2017(7): 11.

[8] KIM D, CHOI S, PARK S, et al. Stereoscopic visual fatigue measurement based on fusional response curve and eye-blinks［C］//The 17th International Conference on Digital Signal Processing, IEEE, 2011: 1-6.

[9] ZHAN Z H, ZHANG L, HU M, et al. Online learners reading ability detection based on eye-tracking sensors［J］. Sensors, 2016, 16(9): 1457.

[10] LIU F, DENG D, LI P. Dynamic context-aware event re-cognition based on Markov logic networks［J］. Sensors, 2017, 17(3): 491.

[11] ZHANG Z, LUO D Y, RASIM Y, et al. A vehicle active safety model: vehicle speed control based on driver vigilance detection using wearable EEG and sparse representation［J］. Sensors, 2016, 16(2): 242.

[12] 李陆军, 丁建江, 郑玉军, 等. 基于动态贝叶斯网络的导弹发射症候识别方法［J］. 电光与控制, 2016, 23(11): 9-12.

[13] KIM J G, CHO J D. Simplified relative model to measure visual fatigue in a stereoscopy［C］//IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2011: 2830-2831.

吕立程, 桑胜波. 动态贝叶斯网络的立体视觉疲劳概率评估[J]. 电光与控制, 2019, 26(9): 45. LV Licheng, SANG Shengbo. Stereoscopic Fatigue Probability Assessment of Dynamic Bayesian NetworksL[J]. Electronics Optics & Control, 2019, 26(9): 45.

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