Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking

Author

Wang, Junqiu ; Yagi, Yasushi

Author_Institution

Osaka Univ., Osaka

Volume

17

Issue

2

fYear

2008

Firstpage

235

Lastpage

240

Abstract

We extend the standard mean-shift tracking algorithm to an adaptive tracker by selecting reliable features from color and shape-texture cues according to their descriptive ability. The target model is updated according to the similarity between the initial and current models, and this makes the tracker more robust. The proposed algorithm has been compared with other trackers using challenging image sequences, and it provides better performance.

Keywords

feature extraction; image colour analysis; image sequences; image texture; object detection; target tracking; adaptive real-time object tracking; adaptive tracker; color features; image sequences; mean-shift tracking algorithm; shape-texture features; target model; Cameras; Head; Helium; Histograms; Image sequences; Lighting; Pixel; Robustness; Target tracking; Yagi-Uda antennas; Feature selection; model updating; multicue; visual tracking; Algorithms; Artificial Intelligence; Color; Colorimetry; Computer Systems; Image Enhancement; Image Interpretation, Computer-Assisted; Motion; Pattern Recognition, Automated; Systems Integration;

fLanguage

English

Journal_Title

Image Processing, IEEE Transactions on

Publisher

ieee

ISSN

1057-7149

Type

jour

DOI

10.1109/TIP.2007.914150

Filename

4401720