Pub Date : 2019-05-01DOI: 10.1007/978-3-030-17795-9_14
Koray Ozcan, Anuj Sharma, Skylar Knickerbocker, Jennifer Merickel, Neal Hawkins, Matthew Rizzo
Automated interpretation and understanding of the driving environment using image processing is a challenging task, as most current vision-based systems are not designed to work in dynamically-changing and naturalistic real-world settings. For instance, road weather condition classification using a camera is a challenge due to high variance in weather, road layout, and illumination conditions. Most transportation agencies, within the U.S., have deployed some cameras for operational awareness. Given that weather related crashes constitute 22% of all vehicle crashes and 16% of crash fatalities, this study proposes using these same cameras as a source for estimating roadway surface condition. The developed model is focused on three road surface conditions resulting from weather including: Clear (clear/dry), Rainy-Wet (rainy/slushy/wet), and Snow (snow-covered/partially snow-covered). The camera sources evaluated are both fixed Closed-circuit Television (CCTV) and mobile (snow plow dash-cam). The results are promising; with an achieved 98.57% and 77.32% road weather classification accuracy for CCTV and mobile cameras, respectively. Proposed classification method is suitable for autonomous selection of snow plow routes and verification of extreme road conditions on roadways.
{"title":"Road Weather Condition Estimation Using Fixed and Mobile Based Cameras.","authors":"Koray Ozcan, Anuj Sharma, Skylar Knickerbocker, Jennifer Merickel, Neal Hawkins, Matthew Rizzo","doi":"10.1007/978-3-030-17795-9_14","DOIUrl":"https://doi.org/10.1007/978-3-030-17795-9_14","url":null,"abstract":"<p><p>Automated interpretation and understanding of the driving environment using image processing is a challenging task, as most current vision-based systems are not designed to work in dynamically-changing and naturalistic real-world settings. For instance, road weather condition classification using a camera is a challenge due to high variance in weather, road layout, and illumination conditions. Most transportation agencies, within the U.S., have deployed some cameras for operational awareness. Given that weather related crashes constitute 22% of all vehicle crashes and 16% of crash fatalities, this study proposes using these same cameras as a source for estimating roadway surface condition. The developed model is focused on three road surface conditions resulting from weather including: Clear (clear/dry), Rainy-Wet (rainy/slushy/wet), and Snow (snow-covered/partially snow-covered). The camera sources evaluated are both fixed Closed-circuit Television (CCTV) and mobile (snow plow dash-cam). The results are promising; with an achieved 98.57% and 77.32% road weather classification accuracy for CCTV and mobile cameras, respectively. Proposed classification method is suitable for autonomous selection of snow plow routes and verification of extreme road conditions on roadways.</p>","PeriodicalId":72090,"journal":{"name":"Advances in computer vision : proceedings of the 2019 Computer Vision Conference (CVC). Volume 1. Computer Vision Conference","volume":"943 ","pages":"192-204"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-030-17795-9_14","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9526616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-25DOI: 10.1007/978-3-030-17795-9_17
M. Reyes, D. Neuhoff, T. Pappas
{"title":"MAP Interpolation of an Ising Image Block","authors":"M. Reyes, D. Neuhoff, T. Pappas","doi":"10.1007/978-3-030-17795-9_17","DOIUrl":"https://doi.org/10.1007/978-3-030-17795-9_17","url":null,"abstract":"","PeriodicalId":72090,"journal":{"name":"Advances in computer vision : proceedings of the 2019 Computer Vision Conference (CVC). Volume 1. Computer Vision Conference","volume":"18 1","pages":"237-256"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87630317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-25DOI: 10.1007/978-3-030-17795-9_3
Javier Díaz-cely, Carlos A. Arce-Lopera, J. Mena, Lina Marcela Quintero
{"title":"The Effect of Color Channel Representations on the Transferability of Convolutional Neural Networks","authors":"Javier Díaz-cely, Carlos A. Arce-Lopera, J. Mena, Lina Marcela Quintero","doi":"10.1007/978-3-030-17795-9_3","DOIUrl":"https://doi.org/10.1007/978-3-030-17795-9_3","url":null,"abstract":"","PeriodicalId":72090,"journal":{"name":"Advances in computer vision : proceedings of the 2019 Computer Vision Conference (CVC). Volume 1. Computer Vision Conference","volume":"1 1","pages":"27-38"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81953957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-25DOI: 10.1007/978-3-030-17795-9_47
S. Purkayastha, Surendra Babu Buddi, Siddhartha Nuthakki, B. Yadav, J. Gichoya
{"title":"Evaluating the Implementation of Deep Learning in LibreHealth Radiology on Chest X-Rays","authors":"S. Purkayastha, Surendra Babu Buddi, Siddhartha Nuthakki, B. Yadav, J. Gichoya","doi":"10.1007/978-3-030-17795-9_47","DOIUrl":"https://doi.org/10.1007/978-3-030-17795-9_47","url":null,"abstract":"","PeriodicalId":72090,"journal":{"name":"Advances in computer vision : proceedings of the 2019 Computer Vision Conference (CVC). Volume 1. Computer Vision Conference","volume":"82 1","pages":"648-657"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74223960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-25DOI: 10.1007/978-3-030-17795-9_46
Xianping Li, J. Martínez
{"title":"Quantitative Comparison of White Matter Segmentation for Brain MR Images","authors":"Xianping Li, J. Martínez","doi":"10.1007/978-3-030-17795-9_46","DOIUrl":"https://doi.org/10.1007/978-3-030-17795-9_46","url":null,"abstract":"","PeriodicalId":72090,"journal":{"name":"Advances in computer vision : proceedings of the 2019 Computer Vision Conference (CVC). Volume 1. Computer Vision Conference","volume":"16 1","pages":"639-647"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87490231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-04-25DOI: 10.1007/978-3-030-17795-9_6
Wenwen Tu, Heng Liu
{"title":"Transfer Probability Prediction for Traffic Flow with Bike Sharing Data: A Deep Learning Approach","authors":"Wenwen Tu, Heng Liu","doi":"10.1007/978-3-030-17795-9_6","DOIUrl":"https://doi.org/10.1007/978-3-030-17795-9_6","url":null,"abstract":"","PeriodicalId":72090,"journal":{"name":"Advances in computer vision : proceedings of the 2019 Computer Vision Conference (CVC). Volume 1. Computer Vision Conference","volume":"1 1","pages":"71-85"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77069228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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