Rongyong Zhao, Yan Wang, Ping Jia, Cuiling Li, Daheng Dong, Yunlong Ma
{"title":"考虑大厅大跨度空间的视频行人分组模型","authors":"Rongyong Zhao, Yan Wang, Ping Jia, Cuiling Li, Daheng Dong, Yunlong Ma","doi":"10.1016/j.jmse.2022.12.005","DOIUrl":null,"url":null,"abstract":"<div><p>Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.</p></div>","PeriodicalId":36172,"journal":{"name":"Journal of Management Science and Engineering","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Video-based pedestrian grouping model considering long-span space in a big hall\",\"authors\":\"Rongyong Zhao, Yan Wang, Ping Jia, Cuiling Li, Daheng Dong, Yunlong Ma\",\"doi\":\"10.1016/j.jmse.2022.12.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.</p></div>\",\"PeriodicalId\":36172,\"journal\":{\"name\":\"Journal of Management Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Management Science and Engineering\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2096232023000185\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Management Science and Engineering","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096232023000185","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Video-based pedestrian grouping model considering long-span space in a big hall
Pedestrian group detection is a challenging but significant issue in pedestrian flow control and public safety management. To address the issue that most conventional pedestrian grouping models (PGMs) can only identify a pedestrian group at a limited distance of less than 2 m, this study extended the pedestrian distance constraint of conventional PGMs with a reconstruction of the normal group detection criterion and development of a novel group detection criterion suitable for long-span space. To measure the movement behavior similarity with normal distance, five necessary constraints: velocity difference, moving direction offset, distance limitation, distance fluctuation, and group-keeping duration were studied quantitatively to form the criterion to detect normal groups. Meanwhile, a long-span group detection criterion was proposed with extended distance and direction consistency constraints. Therefore, this study proposed an improved PGM that considers long-span spaces (PGMLS). In the PGMLS workflow, the MMTrack algorithm was used to obtain pedestrian trajectories. A difference measurement method based on sequential pattern analysis (SPA) was adopted to analyze the velocity similarity of pedestrians. To validate the proposed grouping model, experiments based on pedestrian movement videos in the exit hall of the Shanghai Hongqiao International Airport were conducted. The results indicate that the proposed model can detect both normal and widely separated pedestrian groups, with a long span range of 2–12 m.
期刊介绍:
The Journal of Engineering and Applied Science (JEAS) is the official journal of the Faculty of Engineering, Cairo University (CUFE), Egypt, established in 1816.
The Journal of Engineering and Applied Science publishes fundamental and applied research articles and reviews spanning different areas of engineering disciplines, applications, and interdisciplinary topics.