Motion estimation is the basic need for the success of many video analysis algorithms such as moving object detection, human activity recognition, etc. Most of the motion estimation algorithms are prone to weather conditions and thus, they fail to estimate the motion in degraded weather. Severe weather situations like snow, rain, haze, smog, etc., degrades the performance and reliability of video analysis algorithms. In this paper, we have analyzed the effect of the haze on motion estimation in hazy videos. We propose a cascaded architecture i.e. haze removal followed by optical flow for motion estimation in hazy videos. The proposed image de-hazing network is build upon the Residual and Inception module concepts and named as ResINet. Further, an optical flow is utilized to estimate the motion information. We have carried out the visual analysis to validate the proposed approach for motion estimation in hazy videos. Also, to validate the proposed ResINet for de-hazing, we carried out the quantitative analysis on two benchmark image de-hazing datasets.

运动估计是许多视频分析算法成功的基本需要，例如运动物体检测、人体活动识别等。大多数运动估计算法容易受到天气条件的影响，因此无法估计退化天气下的运动。雪、雨、雾霾、雾霾等恶劣天气情况会降低视频分析算法的性能和可靠性。在本文中，我们分析了雾霾对朦胧视频中运动估计的影响。我们提出了一种级联架构，即去除雾霾，然后是光流，用于在朦胧视频中进行运动估计。建议的图像去雾网络建立在Res idual 和Inception 模块概念并命名为 ResINet。此外，利用光流来估计运动信息。我们已经进行了视觉分析来验证所提出的模糊视频运动估计方法。此外，为了验证建议的 ResINet 去雾，我们对两个基准图像去雾数据集进行了定量分析。