{"title":"Effective Reflection Suppression Method for Vehicle Detection in Complex Nighttime Traffic Scenes","authors":"W. Tsai, Hung-Ju Chen","doi":"10.2352/j.imagingsci.technol.2020.64.4.040402","DOIUrl":null,"url":null,"abstract":"Abstract Headlight is the most explicit and stable image feature in nighttime scenes. This study proposes a headlight detection and pairing algorithm that adapts to numerous scenes to achieve accurate vehicle detection in the nighttime. This algorithm improved the conventional\n histogram equalization by using the difference before and after the equalization to suppress the ground reflection and noise. Then, headlight detection was completed based on this difference as a feature. In addition, the authors combined coordinate information, moving distance, symmetry,\n and stable time to implement headlight pairing, thus enabling vehicle detection in the nighttime. This study effectively overcame complex scenes such as high-speed movement, multi-headlight, and rains. Finally, the algorithm was verified by videos of highway scenes; the detection rate was\n as high as 96.67%. It can be implemented on the Raspberry Pi embedded platform, and its execution speed can reach 25 frames per second.","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"64 1","pages":"40402-1-40402-9"},"PeriodicalIF":0.6000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Imaging Science and Technology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.2352/j.imagingsci.technol.2020.64.4.040402","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract Headlight is the most explicit and stable image feature in nighttime scenes. This study proposes a headlight detection and pairing algorithm that adapts to numerous scenes to achieve accurate vehicle detection in the nighttime. This algorithm improved the conventional
histogram equalization by using the difference before and after the equalization to suppress the ground reflection and noise. Then, headlight detection was completed based on this difference as a feature. In addition, the authors combined coordinate information, moving distance, symmetry,
and stable time to implement headlight pairing, thus enabling vehicle detection in the nighttime. This study effectively overcame complex scenes such as high-speed movement, multi-headlight, and rains. Finally, the algorithm was verified by videos of highway scenes; the detection rate was
as high as 96.67%. It can be implemented on the Raspberry Pi embedded platform, and its execution speed can reach 25 frames per second.
期刊介绍：
Typical issues include research papers and/or comprehensive reviews from a variety of topical areas. In the spirit of fostering constructive scientific dialog, the Journal accepts Letters to the Editor commenting on previously published articles. Periodically the Journal features a Special Section containing a group of related— usually invited—papers introduced by a Guest Editor. Imaging research topics that have coverage in JIST include:
Digital fabrication and biofabrication;
Digital printing technologies;
3D imaging: capture, display, and print;
Augmented and virtual reality systems;
Mobile imaging;
Computational and digital photography;
Machine vision and learning;
Data visualization and analysis;
Image and video quality evaluation;
Color image science;
Image archiving, permanence, and security;
Imaging applications including astronomy, medicine, sports, and autonomous vehicles.
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