TY - GEN
T1 - A shape derivative based approach for crowd flow segmentation
AU - Wu, Si
AU - Yu, Zhiwen
AU - Wong, Hau-San
PY - 2010
Y1 - 2010
N2 - Crowd movement analysis has many practical applications, especially for video surveillance. The common methods are based on pedestrian detection and tracking. With an increase of crowd density, however, it is difficult for these methods to analyze crowd movement because of the computation and complexity. In this paper, a novel approach for crowd flow segmentation is proposed. We employ a Weighting Fuzzy C-Means clustering algorithm (WFCM) to extract the motion region in optical flow field. In order to further analyze crowd movement, we make use of translation flow to approximate local crowd movement, and design a shape derivative based region growing scheme to segment the crowd flows. In the experiments, the proposed method is tested on a set of crowd video sequences from low density to high density. © Springer-Verlag 2010.
AB - Crowd movement analysis has many practical applications, especially for video surveillance. The common methods are based on pedestrian detection and tracking. With an increase of crowd density, however, it is difficult for these methods to analyze crowd movement because of the computation and complexity. In this paper, a novel approach for crowd flow segmentation is proposed. We employ a Weighting Fuzzy C-Means clustering algorithm (WFCM) to extract the motion region in optical flow field. In order to further analyze crowd movement, we make use of translation flow to approximate local crowd movement, and design a shape derivative based region growing scheme to segment the crowd flows. In the experiments, the proposed method is tested on a set of crowd video sequences from low density to high density. © Springer-Verlag 2010.
KW - Crowd flow segmentation
KW - Region growing scheme
KW - Shape derivative
KW - Translation domain
UR - http://www.scopus.com/inward/record.url?scp=78650438234&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-78650438234&origin=recordpage
U2 - 10.1007/978-3-642-12307-8_9
DO - 10.1007/978-3-642-12307-8_9
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 3642123066
SN - 9783642123061
VL - 5994 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 93
EP - 102
BT - Computer Vision, ACCV 2009
PB - Springer Verlag
T2 - 9th Asian Conference on Computer Vision, ACCV 2009
Y2 - 23 September 2009 through 27 September 2009
ER -