Rain Removal in Traffic Surveillance: Does it Matter?

Varying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms. One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms. It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms. However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images. Currently, their behavior is unknown on real-world videos when integrated with a typical computer vision pipeline. In this paper, we review the existing rain removal algorithms and propose a new dataset that consists of 22 traffic surveillance sequences under a broad variety of weather conditions that all include either rain or snowfall. We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow. If successful, the de-rained frames of a rain removal algorithm should improve segmentation performance and increase the number of accurately tracked features. The results show that a recent single-frame-based rain removal algorithm increases the segmentation performance by 19.7% on our proposed dataset, but it eventually decreases the feature tracking performance and showed mixed results with recent instance segmentation methods. However, the best video-based rain removal algorithm improves the feature tracking accuracy by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System

DETECTION OF UNFOCUSED RAINDROPS ON CAR WINDSCREEN COMPARATIVE ANALYSIS USING BACKGROUND SUBRACTIONAND AND WATERSHED ALGORTIHM

Use of ADAS in top end cars has been prevalent over past decade. Electronic control and assistance in cars has proven to be a major feature resulting in passenger safety, saving lives as well as preventing fatalities. This system can be trusted or counted upon in clear weather conditions, which by now has been the only limitation questioning the usefulness of ADAS. Current research focuses to strengthen ADAS in rainy climatic conditions. This paper puts forth a novel idea to detect raindrops where ADAS can be used to increase its functionality in rainy condition to control the speed of over-speeding cars. The method basically includes image database on which Background Subtraction and Watershed algorithm are run to find out a numerical data, and to measure performance of both the method. This data can be used to improve ADAS performance in rainy conditions