3D trajectory recovery for tracking multiple objects and trajectoryguided recognition of actions
Rosales, R.
Sclaroff, S.
Dept. of Comput. Sci., Boston Univ., MA;
This paper appears in: Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Publication Date: 1999
Volume: 2,
On page(s): -123 Vol. 2
Meeting Date: 06/23/1999 - 06/25/1999
Location: Fort Collins, CO, USA
ISBN: 0-7695-0149-4
References Cited: 30
INSPEC Accession Number: 6338769
Digital Object Identifier: 10.1109/CVPR.1999.784618
Current Version Published: 2002-08-06
Abstract
A mechanism is proposed that integrates low-level (image
processing), mid-level (recursive 3D trajectory estimation), and high
level (action recognition) processes. It is assumed that the system
observes multiple moving objects via a single, uncalibrated video
camera. A novel extended Kalman filter formulation is used in estimating
the relative 3D motion trajectories up to a scale factor. The recursive
estimation process provides a prediction and error measure that is
exploited in higher-level stages of action recognition. Conversely,
higher-level mechanisms provide feedback that allows the system to
reliable segment and maintain the tracking of moving objects before,
during, and after occlusion. The 3D trajectory, occlusion, and
segmentation information are utilized in extracting stabilized views of
the moving object. Trajectory-guided recognition (TGR) is proposed as a
new and efficient method for adaptive classification of action. The TGR
approach is demonstrated using “motion history images” that
are then recognized via a mixture of Gaussian classifier. The system was
tested in recognizing various dynamic human outdoor activities; e.g.,
running, walking, roller blading, and cycling. Experiments with
synthetic data sets are used to evaluate stability of the trajectory
estimator with respect to noise
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
