NDLI: Acceleration of vanishing point-based line sampling scheme for people localization and height estimation via 3D line sampling

Content Provider	IEEE Xplore Digital Library
Author	Kuo-Hua Lo Chih-Jung Wang Jen-Hui Chuang Hua-Tsung Chen
Copyright Year	2012
Description	Author affiliation: Information & Communications Technology Lab, National Chiao Tung Univ., HsinChu, Taiwan (Hua-Tsung Chen) \|\| Dept. of Computer Science, National Chiao Tung Univ., HsinChu, Taiwan (Kuo-Hua Lo; Chih-Jung Wang; Jen-Hui Chuang)
Abstract	With the popularity of vision-based camera surveillance, the research on people localization appeals to much attention. In this paper, we propose an efficient and effective system capable of locating a crowd of dense people in real time, using multiple cameras. For each camera view, sample lines, originated from a vanishing point, of foreground objects are projected on the ground plane. Ground regions containing a high density of projected lines are then used to find people locations. Enhanced from previous works, the people localization approach proposed in this paper needs not project all foreground pixels of all views to multiple reference planes or compute pairwise intersections of projected sample lines at different heights, resulting in significant improvement in computational efficiency. Furthermore, the people heights can also be estimated. Experimental results on real surveillance scenes show that comparable accuracy in people localization can be achieved with five times in computing speed compared with our previous approach.
Starting Page	2788
Ending Page	2791
File Size	939718
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Cameras Estimation Surveillance Pattern analysis Image reconstruction Pattern recognition
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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