{"title":"基于CiteSpace的目标跟踪技术研究进展及趋势分析","authors":"Liang Yu","doi":"10.1109/ICSP54964.2022.9778337","DOIUrl":null,"url":null,"abstract":"Object tracking technology is an important research direction in the field of computer vision, and nowadays, it has played an increasingly indispensable role in the fields of intelligent surveillance, modern military, human-computer interaction, intelligent transportation, etc. While making breakthroughs, it has also brought great convenience to people’s lives. In this paper, CiteSpace information visualization software is used to visualize the object tracking research literature based on nearly 10,000 papers in the field of object tracking from 2001 to 2021. From the bibliometric perspective, the visual knowledge graphs of information on three aspects of object tracking technology: scientific cooperation, research fields, and recent progress are analyzed, in which the analysis of scientific cooperation includes the distribution of countries and institutions, the analysis of research fields includes the distribution of categories and keywords, and the analysis of recent progress and frontiers includes the distribution of references and the analysis of keyword timeline graph. Then, we present the three main problems and challenges facing this technology: changes of illumination and color, changes of scene and posture, and the distinction between foreground and background. And three targeted suggestions are proposed for feature fusion, 3D reconstruction, and deeper development based on Siamese Network in deep learning algorithms. Finally, the future development of object tracking technology has been prospected.","PeriodicalId":363766,"journal":{"name":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research progress and trend analysis of object tracking technology based on CiteSpace\",\"authors\":\"Liang Yu\",\"doi\":\"10.1109/ICSP54964.2022.9778337\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Object tracking technology is an important research direction in the field of computer vision, and nowadays, it has played an increasingly indispensable role in the fields of intelligent surveillance, modern military, human-computer interaction, intelligent transportation, etc. While making breakthroughs, it has also brought great convenience to people’s lives. In this paper, CiteSpace information visualization software is used to visualize the object tracking research literature based on nearly 10,000 papers in the field of object tracking from 2001 to 2021. From the bibliometric perspective, the visual knowledge graphs of information on three aspects of object tracking technology: scientific cooperation, research fields, and recent progress are analyzed, in which the analysis of scientific cooperation includes the distribution of countries and institutions, the analysis of research fields includes the distribution of categories and keywords, and the analysis of recent progress and frontiers includes the distribution of references and the analysis of keyword timeline graph. Then, we present the three main problems and challenges facing this technology: changes of illumination and color, changes of scene and posture, and the distinction between foreground and background. And three targeted suggestions are proposed for feature fusion, 3D reconstruction, and deeper development based on Siamese Network in deep learning algorithms. Finally, the future development of object tracking technology has been prospected.\",\"PeriodicalId\":363766,\"journal\":{\"name\":\"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-04-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSP54964.2022.9778337\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSP54964.2022.9778337","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research progress and trend analysis of object tracking technology based on CiteSpace
Object tracking technology is an important research direction in the field of computer vision, and nowadays, it has played an increasingly indispensable role in the fields of intelligent surveillance, modern military, human-computer interaction, intelligent transportation, etc. While making breakthroughs, it has also brought great convenience to people’s lives. In this paper, CiteSpace information visualization software is used to visualize the object tracking research literature based on nearly 10,000 papers in the field of object tracking from 2001 to 2021. From the bibliometric perspective, the visual knowledge graphs of information on three aspects of object tracking technology: scientific cooperation, research fields, and recent progress are analyzed, in which the analysis of scientific cooperation includes the distribution of countries and institutions, the analysis of research fields includes the distribution of categories and keywords, and the analysis of recent progress and frontiers includes the distribution of references and the analysis of keyword timeline graph. Then, we present the three main problems and challenges facing this technology: changes of illumination and color, changes of scene and posture, and the distinction between foreground and background. And three targeted suggestions are proposed for feature fusion, 3D reconstruction, and deeper development based on Siamese Network in deep learning algorithms. Finally, the future development of object tracking technology has been prospected.