[论文解读] Expressway visibility estimation based on image entropy and piecewise stationary time series analysis

Xiaogang Cheng, Guoqing Liu+3 more|arXiv (Cornell University)|Apr 8, 2018

Image Enhancement Techniques参考文献 21被引用 6

一句话总结

本文提出了一种新颖的数据驱动方法SPEV，通过高斯图像熵和分段平稳时间序列分析，实现对高速公路大气能见度的估计。通过利用感兴趣区域分析和相对熵比，有效缓解了光照和场景变化的影响，该模型在采集自中国雾多发的通启高速公路的两百万段视频中，实现了99.14%的预测结果相对误差低于10%。

ABSTRACT

Vision-based methods for visibility estimation can play a critical role in reducing traffic accidents caused by fog and haze. To overcome the disadvantages of current visibility estimation methods, we present a novel data-driven approach based on Gaussian image entropy and piecewise stationary time series analysis (SPEV). This is the first time that Gaussian image entropy is used for estimating atmospheric visibility. To lessen the impact of landscape and sunshine illuminance on visibility estimation, we used region of interest (ROI) analysis and took into account relative ratios of image entropy, to improve estimation accuracy. We assume fog and haze cause blurred images and that fog and haze can be considered as a piecewise stationary signal. We used piecewise stationary time series analysis to construct the piecewise causal relationship between image entropy and visibility. To obtain a real-world visibility measure during fog and haze, a subjective assessment was established through a study with 36 subjects who performed visibility observations. Finally, a total of two million videos were used for training the SPEV model and validate its effectiveness. The videos were collected from the constantly foggy and hazy Tongqi expressway in Jiangsu, China. The contrast model of visibility estimation was used for algorithm performance comparison, and the validation results of the SPEV model were encouraging as 99.14% of the relative errors were less than 10%.

研究动机与目标

• 解决现有基于视觉的能见度估计方法在雾和霾条件下的局限性。
• 降低可变光照和场景内容对能见度估计精度的影响。
• 开发一种数据驱动方法，以建模图像熵与大气能见度之间的因果关系。
• 利用来自持续多雾多霾高速公路的真实世界视频数据验证该模型。
• 通过36名参与者的规模化主观评估，建立可靠且客观的能见度测量基准。

提出的方法

• 计算高斯图像熵作为图像模糊程度的度量，假设雾和霾因降低图像清晰度而增加熵值。
• 应用感兴趣区域（ROI）分析，聚焦于相关道路区域，减少背景干扰。
• 利用ROI与全图之间的相对熵比，对光照和场景内容变化进行归一化。
• 将能见度建模为分段平稳时间序列，假设雾/霾引入了特征明显且稳定的信号段。
• 应用分段平稳时间序列分析，建立图像熵与能见度之间的因果性、时变关系。
• 在江苏省通启高速公路上采集的两百万段视频剪辑上训练并验证SPEV模型。

实验结果

研究问题

• RQ1 高斯图像熵能否作为雾和霾条件下大气能见度的可靠代理指标？
• RQ2 如何有效归一化场景内容和光照变化，以提升能见度估计的精度？
• RQ3 将能见度-熵关系建模为分段平稳时间序列是否能提升预测的鲁棒性？
• RQ4 在持续多雾环境的真实世界视频数据上，基于数据驱动的方法可实现何种精度的估计？
• RQ5 与现有基于对比度的能见度模型相比，该方法在误差分布方面表现如何？

主要发现

• SPEV模型在测试数据集上，99.14%的能见度预测结果相对误差低于10%。
• 采用感兴趣区域分析和相对熵比显著提升了对光照和场景变化的鲁棒性。
• 分段平稳时间序列模型有效捕捉了雾天条件下图像熵与能见度之间的因果动态。
• 通过36名参与者的主观能见度评估，为模型验证提供了可靠的基准真值。
• 该模型在通启高速公路的真实世界视频数据上表现出强大的泛化能力，该地点以持续多雾多霾著称。
• 与基于对比度的模型相比，SPEV在误差分布和不同能见度条件下的表现一致性方面表现更优。