{"title":"多目标城市数据集:检测行人、交通和摩托车的资源","authors":"","doi":"10.1016/j.dib.2024.110887","DOIUrl":null,"url":null,"abstract":"<div><p>This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization. Our main goal is to assist academics, urban planners, and decision-makers in creating sophisticated models for pedestrian safety, traffic control, and accident avoidance. This dataset is a useful resource for training and verifying algorithms targeted at boosting real-time traffic monitoring systems, optimizing urban infrastructure, and <em>raising</em> overall road safety because of its high variability and significant volume. This dataset represents a major advancement for smart city projects and the creation of intelligent transportation systems.</p></div>","PeriodicalId":10973,"journal":{"name":"Data in Brief","volume":null,"pages":null},"PeriodicalIF":1.0000,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352340924008503/pdfft?md5=376d9fac7d42a2b11e0962898cfb9ff8&pid=1-s2.0-S2352340924008503-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Multi-object urban dataset: A resource for detecting pedestrians, traffic and motorbikes\",\"authors\":\"\",\"doi\":\"10.1016/j.dib.2024.110887\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization. Our main goal is to assist academics, urban planners, and decision-makers in creating sophisticated models for pedestrian safety, traffic control, and accident avoidance. This dataset is a useful resource for training and verifying algorithms targeted at boosting real-time traffic monitoring systems, optimizing urban infrastructure, and <em>raising</em> overall road safety because of its high variability and significant volume. This dataset represents a major advancement for smart city projects and the creation of intelligent transportation systems.</p></div>\",\"PeriodicalId\":10973,\"journal\":{\"name\":\"Data in Brief\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2024-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2352340924008503/pdfft?md5=376d9fac7d42a2b11e0962898cfb9ff8&pid=1-s2.0-S2352340924008503-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data in Brief\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352340924008503\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data in Brief","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352340924008503","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Multi-object urban dataset: A resource for detecting pedestrians, traffic and motorbikes
This article describes a dataset comprising 16,426 real-world urban photographs, capturing vehicles, cyclists, motorbikes, and pedestrians across Morning, Evening, and Night scenes. The dataset is valuable for machine learning tasks in traffic analysis, urban planning, and public safety. It enables the development and validation of algorithms for pedestrian detection, traffic flow analysis, and infrastructure optimization. Our main goal is to assist academics, urban planners, and decision-makers in creating sophisticated models for pedestrian safety, traffic control, and accident avoidance. This dataset is a useful resource for training and verifying algorithms targeted at boosting real-time traffic monitoring systems, optimizing urban infrastructure, and raising overall road safety because of its high variability and significant volume. This dataset represents a major advancement for smart city projects and the creation of intelligent transportation systems.
期刊介绍:
Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.