A Wide Area Multiview Static Crowd Estimation System Using UAV and 3D Training Simulator

Math Erthygl
Iaith wreiddiolSaesneg
Rhif yr erthygle2780
Nifer y tudalennau33
CyfnodolynRemote Sensing
Cyfrol13
Rhif y cyfnodolyn14
Dyddiad ar-lein cynnar15 Gorff 2021
Dangosyddion eitem ddigidol (DOIs)
StatwsE-gyhoeddi cyn argraffu - 15 Gorff 2021
Cysylltiad parhaol
Arddangos ystadegau lawrlwytho
Gweld graff cysylltiadau
Fformatau enwi

Crynodeb

Crowd size estimation is a challenging problem, especially when the crowd is spread over a significant geographical area. It has applications in monitoring of rallies and demonstrations and in calculating the assistance requirements in humanitarian disasters. Therefore, accomplishing a crowd surveillance system for large crowds constitutes a significant issue. UAV-based techniques are an appealing choice for crowd estimation over a large region, but they present a variety of interesting challenges, such as integrating per-frame estimates through a video without counting individuals twice. Large quantities of annotated training data are required to design, train, and test such a system. In this paper, we have first reviewed several crowd estimation techniques, existing crowd simulators and data sets available for crowd analysis. Later, we have described a simulation system to provide such data, avoiding the need for tedious and error-prone manual annotation. Then, we have evaluated synthetic video from the simulator using various existing single-frame crowd estimation techniques. Our findings show that the simulated data can be used to train and test crowd estimation, thereby providing a suitable platform to develop such techniques. We also propose an automated UAV-based 3D crowd estimation system that can be used for approximately static or slow-moving crowds, such as public events, political rallies, and natural or man-made disasters. We evaluate the results by applying our new framework to a variety of scenarios with varying crowd sizes. The proposed system gives promising results using widely accepted metrics including MAE, RMSE, Precision, Recall, and F1 score to validate the results.

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