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Open AccessReview

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

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School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
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Institute of Smart City, Shanghai University, Shanghai 200444, China
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Department of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla 72810, Mexico
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Sydney Institute of Language and Commerce, Shanghai University, Shanghai 200444, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 440; https://doi.org/10.3390/ijgi8100440
Received: 5 August 2019 / Revised: 18 September 2019 / Accepted: 3 October 2019 / Published: 7 October 2019
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.
Keywords: urban crowd flow analysis; feature extraction; spatio-temporal data; big data; social media urban crowd flow analysis; feature extraction; spatio-temporal data; big data; social media
MDPI and ACS Style

Ebrahimpour, Z.; Wan, W.; Cervantes, O.; Luo, T.; Ullah, H. Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis. ISPRS Int. J. Geo-Inf. 2019, 8, 440.

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