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Object Classification Method using Hilbert Scanning Distance  

Choi, Jeong-Hwan (서울대 공과대학 전기컴퓨터공학과)
Baek, Young-Min (서울대 공과대학 전기컴퓨터공학과)
Choi, Jin-Young (서울대 공과대학 전기컴퓨터공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.57, no.4, 2008 , pp. 700-705 More about this Journal
Abstract
In this paper, we propose object classification algorithm for real-time surveillance system. We have approached this problem using silhouette-based template matching. The silhouette of the object is extracted, and then it is compared with representative template models. Template models are previously stored in the database. Our algorithm is similar to previous pixel-based template matching scheme like Hausdorff Distance, but we use 1D image array rather than 2D regions inspired by Hilbert Path. Transformation of images could reduce computational burden to compute similarity between the detected image and the template images. Experimental results show robustness and real-time performance in object classification, even in low resolution images.
Keywords
Hilbert Scan Distance; Template Matching; Object Classification; Surveillance System;
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