Paper Info
Title
View Independent Gait Identification Using a Particle Filter
Abstract
We challenge the human identification problem from the perspective of gait and body shape. Conventional methods depend on the camera viewing direction, and since they are based on matching image silhouettes or features their identification accuracy is low when there is a big difference between the camera viewing direction of the test and training data. Thus, if a person is walking in an arbitrary direction, they may not be accurately identified. In this paper, we propose a novel method that does not depend on the camera viewing direction. We develop a state space model called a "cyclic motion model" whose state variables are not only the phase of the motions but also the camera viewing direction. We learn model parameters for each candidate person, and represent their walking with the cyclic motion model. To identify a person from the observed image sequence, we first compute the model likelihoods for the sequence using a particle filter that represents a probability distribution by a set of weighted samples, We then identify the person from model likelihoods.
Year
DOI
Venue
2006
10.1109/AVSS.2006.116
AVSS
Keywords
Field
DocType
state space model,view independent gait identification,arbitrary direction,camera viewing direction,candidate person,cyclic motion model,identification accuracy,model parameter,particle filter,model likelihood,human identification problem,image silhouette,shape,particle filters,probability distribution,training data,body shape,distributed computing,testing
Training set,Computer vision,Pattern recognition,Gait,Computer science,State-space representation,Particle filter,Probability distribution,Artificial intelligence,State variable,Image sequence,Parameter identification problem
Conference
ISBN
Citations 
PageRank 
0-7695-2688-8
0
0.34
References 
Authors
6
4
Name
Order
Citations
PageRank
Mitsuharu Emoto100.34
Akira Hayashi2519.08
Nobuo Suematsu3548.99
Kazunori Iwata48029.80