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Proceedings Paper

Gait curves for human recognition, backpack detection, and silhouette correction in a nighttime environment
Author(s): Brian DeCann; Arun Ross
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Paper Abstract

The need for an automated surveillance system is pronounced at night when the capability of the human eye to detect anomalies is reduced. While there have been significant efforts in the classification of individuals using human metrology and gait, the majority of research assumes a day-time environment. The aim of this study is to move beyond traditional image acquisition modalities and explore the issues of object detection and human identification at night. To address these issues, a spatiotemporal gait curve that captures the shape dynamics of a moving human silhouette is employed. Initially proposed by Wang et al., this representation of the gait is expanded to incorporate modules for individual classification, backpack detection, and silhouette restoration. Evaluation of these algorithms is conducted on the CASIA Night Gait Database, which includes 10 video sequences for each of 153 unique subjects. The video sequences were captured using a low resolution thermal camera. Matching performance of the proposed algorithms is evaluated using a nearest neighbor classifier. The outcome of this work is an efficient algorithm for backpack detection and human identification, and a basis for further study in silhouette enhancement.

Paper Details

Date Published: 14 April 2010
PDF: 13 pages
Proc. SPIE 7667, Biometric Technology for Human Identification VII, 76670Q (14 April 2010); doi: 10.1117/12.851296
Show Author Affiliations
Brian DeCann, West Virginia Univ. (United States)
Arun Ross, West Virginia Univ. (United States)

Published in SPIE Proceedings Vol. 7667:
Biometric Technology for Human Identification VII
B. V. K. Vijaya Kumar; Salil Prabhakar; Arun A. Ross, Editor(s)

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