Tracking human motion in structured environments using adistributed-camera system
Cai, Q.
Aggarwal, J.K.
Realnetworks Inc., Seattle, WA;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Nov 1999
Volume: 21,
Issue: 11
On page(s): 1241-1247
ISSN: 0162-8828
References Cited: 10
CODEN: ITPIDJ
INSPEC Accession Number: 6446103
Digital Object Identifier: 10.1109/34.809119
Current Version Published: 2002-08-06
Abstract
This paper presents a comprehensive framework for tracking coarse
human models from sequences of synchronized monocular grayscale images
in multiple camera coordinates. It demonstrates the feasibility of an
end-to-end person tracking system using a unique combination of motion
analysis on 3D geometry in different camera coordinates and other
existing techniques in motion detection, segmentation, and pattern
recognition. The system starts with tracking from a single camera view.
When the system predicts that the active camera will no longer have a
good view of the subject of interest, tracking will be switched to
another camera which provides a better view and requires the least
switching to continue tracking. The nonrigidity of the human body is
addressed by matching points of the middle line of the human image,
spatially and temporally, using Bayesian classification schemes.
Multivariate normal distributions are employed to model
class-conditional densities of the features for tracking, such as
location, intensity, and geometric features. Limited degrees of
occlusion are tolerated within the system. Experimental results using a
prototype system are presented and the performance of the algorithm is
evaluated to demonstrate its feasibility for real time
applications
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