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ICIP
2009
IEEE

K-means based segmentation for real-time zenithal people counting

9 years 7 months ago
K-means based segmentation for real-time zenithal people counting
The paper presents an efficient and reliable approach to automatic people segmentation, tracking and counting, designed for a system with an overhead mounted (zenithal) camera. Upon the initial block-wise background subtraction, k-means clustering is used to enable the segmentation of single persons in the scene. The number of people in the scene is estimated as the maximal number of clusters with acceptable inter-cluster separation. Tracking of segmented people is addressed as a problem of dynamic cluster assignment between two consecutive frames and it is solved in a greedy fashion. Systems for people counting are applied to people surveillance and management and lately within the ambient intelligence solutions. Experimental results suggest that the proposed method is able to achieve very good results in terms of counting accuracy and execution speed.
Borislav Antic, Dragan Letic, Dubravko Culibrk, Vl
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICIP
Authors Borislav Antic, Dragan Letic, Dubravko Culibrk, Vladimir S. Crnojevic
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