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。

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.