Gait Analysis for Classification

Item

Title: en_US Gait Analysis for Classification
Creator: en_US Lee, Lily
Date: 2004-10-20T20:32:06Z
Date Available: 2004-10-20T20:32:06Z
Date Issued: en_US 2003-06-26
Identifier: en_US AITR-2003-014
uri: http://hdl.handle.net/1721.1/7109
Abstract: en_US This thesis describes a representation of gait appearance for the purpose of person identification and classification. This gait representation is based on simple localized image features such as moments extracted from orthogonal view video silhouettes of human walking motion. A suite of time-integration methods, spanning a range of coarseness of time aggregation and modeling of feature distributions, are applied to these image features to create a suite of gait sequence representations. Despite their simplicity, the resulting feature vectors contain enough information to perform well on human identification and gender classification tasks. We demonstrate the accuracy of recognition on gait video sequences collected over different days and times and under varying lighting environments. Each of the integration methods are investigated for their advantages and disadvantages. An improved gait representation is built based on our experiences with the initial set of gait representations. In addition, we show gender classification results using our gait appearance features, the effect of our heuristic feature selection method, and the significance of individual features.
Extent: en_US 110 p.; 4040471 bytes; 994319 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-2003-014
Subject: en_US AI; en_US gait recognition; en_US gender classification

Item sets: AI Technical Reports (1964 - 2004)