{"title":"农业环境下基于事件的视觉数据集的数据验证与格式化过程","authors":"Maris Galauskis, Arturs Ardavs","doi":"10.2478/acss-2021-0021","DOIUrl":null,"url":null,"abstract":"Abstract In this paper, we describe our team’s data processing practice for an event-based camera dataset. In addition to the event-based camera data, the Agri-EBV dataset contains data from LIDAR, RGB, depth cameras, temperature, moisture, and atmospheric pressure sensors. We describe data transfer from a platform, automatic and manual validation of data quality, conversions to multiple formats, and structuring of the final data. Accurate time offset estimation between sensors achieved in the dataset uses IMU data generated by purposeful movements of the sensor platform. Therefore, we also outline partitioning of the data and time alignment calculation during post-processing.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"52 1","pages":"173 - 177"},"PeriodicalIF":0.5000,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Process of Data Validation and Formatting for an Event-Based Vision Dataset in Agricultural Environments\",\"authors\":\"Maris Galauskis, Arturs Ardavs\",\"doi\":\"10.2478/acss-2021-0021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this paper, we describe our team’s data processing practice for an event-based camera dataset. In addition to the event-based camera data, the Agri-EBV dataset contains data from LIDAR, RGB, depth cameras, temperature, moisture, and atmospheric pressure sensors. We describe data transfer from a platform, automatic and manual validation of data quality, conversions to multiple formats, and structuring of the final data. Accurate time offset estimation between sensors achieved in the dataset uses IMU data generated by purposeful movements of the sensor platform. Therefore, we also outline partitioning of the data and time alignment calculation during post-processing.\",\"PeriodicalId\":41960,\"journal\":{\"name\":\"Applied Computer Systems\",\"volume\":\"52 1\",\"pages\":\"173 - 177\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2021-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computer Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/acss-2021-0021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, THEORY & METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computer Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/acss-2021-0021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
The Process of Data Validation and Formatting for an Event-Based Vision Dataset in Agricultural Environments
Abstract In this paper, we describe our team’s data processing practice for an event-based camera dataset. In addition to the event-based camera data, the Agri-EBV dataset contains data from LIDAR, RGB, depth cameras, temperature, moisture, and atmospheric pressure sensors. We describe data transfer from a platform, automatic and manual validation of data quality, conversions to multiple formats, and structuring of the final data. Accurate time offset estimation between sensors achieved in the dataset uses IMU data generated by purposeful movements of the sensor platform. Therefore, we also outline partitioning of the data and time alignment calculation during post-processing.
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