{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rain-removal-in-traffic-surveillance-does-it","title":"Rain Removal in Traffic Surveillance: Does it Matter?","arxiv_id":"1810.12574","date":"2018-10-30","proceeding":null,"authors":["Chris H. Bahnsen","Thomas B. Moeslund"],"abstract":"Varying weather conditions, including rainfall and snowfall, are generally\nregarded as a challenge for computer vision algorithms. One proposed solution\nto the challenges induced by rain and snowfall is to artificially remove the\nrain from images or video using rain removal algorithms. It is the promise of\nthese algorithms that the rain-removed image frames will improve the\nperformance of subsequent segmentation and tracking algorithms. However, rain\nremoval algorithms are typically evaluated on their ability to remove synthetic\nrain on a small subset of images. Currently, their behavior is unknown on\nreal-world videos when integrated with a typical computer vision pipeline. In\nthis paper, we review the existing rain removal algorithms and propose a new\ndataset that consists of 22 traffic surveillance sequences under a broad\nvariety of weather conditions that all include either rain or snowfall. We\npropose a new evaluation protocol that evaluates the rain removal algorithms on\ntheir ability to improve the performance of subsequent segmentation, instance\nsegmentation, and feature tracking algorithms under rain and snow. If\nsuccessful, the de-rained frames of a rain removal algorithm should improve\nsegmentation performance and increase the number of accurately tracked\nfeatures. The results show that a recent single-frame-based rain removal\nalgorithm increases the segmentation performance by 19.7% on our proposed\ndataset, but it eventually decreases the feature tracking performance and\nshowed mixed results with recent instance segmentation methods. However, the\nbest video-based rain removal algorithm improves the feature tracking accuracy\nby 7.72%.","url_abs":"http://arxiv.org/abs/1810.12574v1","url_pdf":"http://arxiv.org/pdf/1810.12574v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rain-removal-in-traffic-surveillance-does-it","repo_url":"https://bitbucket.org/aauvap/aau-rainsnow-eval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"rain-removal-in-traffic-surveillance-does-it","repo_url":"https://bitbucket.org/aauvap/rainremoval","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"rain-removal-in-traffic-surveillance-does-it","repo_url":"https://github.com/chrisbahnsen/aau-rainsnow-eval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"rain-removal-in-traffic-surveillance-does-it","repo_url":"https://github.com/chrisbahnsen/rainremoval","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"rain-removal","task_name":"Rain Removal"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.12574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1810.12574"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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