English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Varying weather conditions, including rainfall and snowfall, are generallyregarded as a challenge for computer vision algorithms. One proposed solutionto the challenges induced by rain and snowfall is to artificially remove therain from images or video using rain removal algorithms. It is the promise ofthese algorithms that the rain-removed image frames will improve theperformance of subsequent segmentation and tracking algorithms. However, rainremoval algorithms are typically evaluated on their ability to remove syntheticrain on a small subset of images. Currently, their behavior is unknown onreal-world videos when integrated with a typical computer vision pipeline. Inthis paper, we review the existing rain removal algorithms and propose a newdataset that consists of 22 traffic surveillance sequences under a broadvariety of weather conditions that all include either rain or snowfall. Wepropose a new evaluation protocol that evaluates the rain removal algorithms ontheir ability to improve the performance of subsequent segmentation, instancesegmentation, and feature tracking algorithms under rain and snow. Ifsuccessful, the de-rained frames of a rain removal algorithm should improvesegmentation performance and increase the number of accurately trackedfeatures. The results show that a recent single-frame-based rain removalalgorithm increases the segmentation performance by 19.7% on our proposeddataset, but it eventually decreases the feature tracking performance andshowed mixed results with recent instance segmentation methods. However, thebest video-based rain removal algorithm improves the feature tracking accuracyby 7.72%.

Rain Removal in Traffic Surveillance: Does it Matter?