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

Rain Streaks Removal from Single Image

Rain removal from video is one of the challenging problems. There are very few methods which address the problem of rain removal from single image. Existing methods removes rain streaks from video not from single image. These methods capture non-rain data from successive images. This data is then utilized to replace rain-part in current images. This approach removes rain streaks from single image. Morphological Component Analysis (MCA) [9 - 13] decomposes image into Low Frequency (LF) and High Frequency (HF) parts using bilateral filter. High frequency part is then decomposed into rain-component and nonrain-component by performing dictionary learning and sparse coding [2]. Non-rain component contains image features from which rain streaks are removed. Non-rain component is mixed with Low Frequency (LF) image component to form original image from which rain steaks are removed. The Morphological Component Analysis (MCA) [9 - 13] is a allows us to separate features contained in an image when these features present different morphological aspects. MCA can be very useful for decomposing images into texture and piecewise smooth (cartoon) parts or for inpainting applications. DOI: 10.17762/ijritcc2321-8169.150615