A Feature Fusion Algorithm for Human Matching between Non-Overlapping Cameras

Author

Lv, Xiaowei ; Kong, Qing-Jie ; Liu, Yuncai

Author_Institution

Inst. of Image Process. & Pattern Recognition, Shanghai Jiao Tong Univ., Shanghai

fYear

2008

fDate

22-24 Oct. 2008

Firstpage

1

Lastpage

6

Abstract

Human matching is fundamental in human tracking over non-overlapping cameras. Fusing multiple features is an efficient way to increase the ratio of matching. In this paper, we present an algorithm of iterative widening fusion (IWF) to fuse the multiple features, including color histogram, UV chromaticity, major color spectrum histogram and scale-invariant features (SIFT). Also, the Bayesian framework, as a classical fusion method, is compared with the IWF algorithm. The experimental results indicated that the IWF algorithm obtained the matching accuracy better than Bayesian framework in most cases.

Keywords

Bayes methods; feature extraction; image colour analysis; image fusion; image matching; iterative methods; optical tracking; Bayesian framework; UV chromaticity; color histogram; feature fusion; human matching; human tracking; iterative widening fusion; major color spectrum histogram; nonoverlapping cameras; scale-invariant features; Bayesian methods; Brightness; Cameras; Fuses; Histograms; Humans; Image processing; Iterative algorithms; Pattern matching; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2008. CCPR '08. Chinese Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-2316-3

Type

conf

DOI

10.1109/CCPR.2008.23

Filename

4662976