Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis
Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approa...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/8/10/440 |
| _version_ | 1818543648718979072 |
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| author | Zeinab Ebrahimpour Wanggen Wan Ofelia Cervantes Tianhang Luo Hidayat Ullah |
| author_facet | Zeinab Ebrahimpour Wanggen Wan Ofelia Cervantes Tianhang Luo Hidayat Ullah |
| author_sort | Zeinab Ebrahimpour |
| collection | DOAJ |
| description | Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed. |
| first_indexed | 2024-12-11T22:38:10Z |
| format | Article |
| id | doaj.art-df4e7de754a44ec6b3e05453e0273b66 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-11T22:38:10Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-df4e7de754a44ec6b3e05453e0273b662022-12-22T00:47:53ZengMDPI AGISPRS International Journal of Geo-Information2220-99642019-10-0181044010.3390/ijgi8100440ijgi8100440Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow AnalysisZeinab Ebrahimpour0Wanggen Wan1Ofelia Cervantes2Tianhang Luo3Hidayat Ullah4School of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaDepartment of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla 72810, MexicoInstitute of Smart City, Shanghai University, Shanghai 200444, ChinaSchool of Communication and Information Engineering, Shanghai University, Shanghai 200444, ChinaExtracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.https://www.mdpi.com/2220-9964/8/10/440urban crowd flow analysisfeature extractionspatio-temporal databig datasocial media |
| spellingShingle | Zeinab Ebrahimpour Wanggen Wan Ofelia Cervantes Tianhang Luo Hidayat Ullah Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis ISPRS International Journal of Geo-Information urban crowd flow analysis feature extraction spatio-temporal data big data social media |
| title | Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis |
| title_full | Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis |
| title_fullStr | Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis |
| title_full_unstemmed | Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis |
| title_short | Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis |
| title_sort | comparison of main approaches for extracting behavior features from crowd flow analysis |
| topic | urban crowd flow analysis feature extraction spatio-temporal data big data social media |
| url | https://www.mdpi.com/2220-9964/8/10/440 |
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