The Community for Technology Leaders
Computer Vision, IEEE International Conference on (2007)
Rio de Janeiro, Brazil
Oct. 14, 2007 to Oct. 21, 2007
ISBN: 978-1-4244-1630-1
pp: 1-8
Abhinav Gupta , Dept. of Computer Science, University of Maryland, College Park, MD, USA. agupta@cs.umd.edu
Anurag Mittal , Dept. of Comp. Sc. and Engg., IIT Madras, Chennai, India. amittal@cse.iitm.ernet.in
Larry S. Davis , Dept. of Computer Science, University of Maryland, College Park, MD, USA. lsd@cs.umd.edu
ABSTRACT
Development of multiple camera based vision systems for analysis of dynamic objects such as humans is challenging due to occlusions and similarity in the appearance of a person with the background and other people- visual "confusion". Since occlusion and confusion depends on the presence of other people in the scene, it leads to a dependency structure where there are often loops in the resulting Bayesian network. While approaches such as loopy belief propagation can be used for inference, they are computationally expensive and convergence is not guaranteed in many situations. We present a unified approach, COST, that reasons about such dependencies and yields an order for the inference of each person in a group of people and a set of cameras to be used for inferences for a person. Using the probabilistic distribution of the positions and appearances of people, COST performs visibility and confusion analysis for each part of each person and computes the amount of information that can be computed with and without more accurate estimation of the positions of other people. We present an optimization problem to select set of cameras and inference dependencies for each person which attempts to minimize the computational cost under given performance constraints. Results show the efficiency of COST in improving the performance of such systems and reducing the computational resources required.
INDEX TERMS
CITATION

A. Gupta, L. S. Davis and A. Mittal, "COST: An Approach for Camera Selection and Multi-Object Inference Ordering in Dynamic Scenes," 2007 11th IEEE International Conference on Computer Vision(ICCV), Rio de Janeiro, 2007, pp. 1-8.
doi:10.1109/ICCV.2007.4408842
101 ms
(Ver 3.3 (11022016))