Efficient human detection in crowded environment
Journal
Multimedia Systems
Journal Volume
21
Journal Issue
2
Pages
177-187
Date Issued
2014
Author(s)
Abstract
Detecting humans in crowded environment is profitable but challenging in video surveillance. We propose an efficient human detection method by combining both motion and appearance clues. Moving pixels are first extracted by background subtraction, and then a filtering step is used to narrow the range for human template matching. We utilize integral images to fast generate shape information from edge maps of each frame and define the matching probability to be capable of detecting both full-body and partial-body. Representative human templates are constructed by sparse contours on the basis of the point distribution model. Moreover, linear regression analysis is also applied to adaptively adjust the template sizes. With the aid of the proposed foreground ratio filtering and the multi-sized template matching techniques, experimental results show that our method not only can efficiently detect humans in a crowded environment, but also largely enhance the resultant detection accuracy. © 2014, Springer-Verlag Berlin Heidelberg.
Subjects
Human detection; Sparse human contour; Surveillance video; Template matching
Other Subjects
Regression analysis; Security systems; Background subtraction; Detection accuracy; Human detection; Point distribution modeling; Sparse human contour; Surveillance video; Template matching technique; Video surveillance; Template matching
Type
journal article