Název: Detecting Dominant Motion Flows and People Counting in High Density Crowds
Autoři: Khan, Sultan Daud
Vizzari, Giuseppe
Bandini, Stefania
Basalamah, Saleh
Citace zdrojového dokumentu: Journal of WSCG. 2014, vol. 22, no. 1, p. 21-30.
Datum vydání: 2014
Nakladatel: Václav Skala - UNION Agency
Typ dokumentu: article
článek
URI: http://wscg.zcu.cz/WSCG2014/!!_2014-Journal-No-1.pdf
http://hdl.handle.net/11025/11897
ISSN: 1213–6972 (hardcopy)
1213–6980 (CD-ROM)
1213–6964 (online)
Klíčová slova: analýza davu;klastrování;segmentace toku;počítání lidí;počítačové zpracování obrazu
Klíčová slova v dalším jazyce: crowd analysis;clustering;flow segmentation;people counting;computer image processing
Abstrakt: Urbanisation is growingly generating crowding situations which generate potential issues for planning and public safety. This paper proposes new techniques of crowd analysis and precisely crowd flow segmentation and crowd counting framework for estimating the number of people in each flow segment. We use two foreground masks, one generated by Horn-Schunck optical flow, used by crowd flow segmentation, and another by Gaussian background subtraction, used by crowd counting framework. For crowd flow segmentation, we adopt K-means clustering algorithm which segments the crowd in different flows. After clustering, some small blobs can appear which are removed by blob absorption method. After blob absorption, crowd flow is segmented into different dominant flows. Finally, we estimate the number of people in each flow segment by using blob analysis and blob size optimization methods. Our experimental results demonstrate the effectiveness of the proposed method comparing to other stateof- the-art approaches and our proposed crowd counting framework estimates the number of people with about 90% accuracy.
Práva: © Václav Skala - UNION Agency
Vyskytuje se v kolekcích:Volume 22, Number 1 (2014)

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