Paper Info
Title
Online Feature Evaluation For Object Tracking Using Kalman Filter
Abstract
An online feature evaluation method for visual object tracking is put forward in this paper. Firstly, a combined feature set is built using color histogram (HC) bins and gradient orientation histogram (HOG) bins considering the color and contour representation of an object respectively. Then a novel method is proposed to evaluate the features' weights in a tracking process using Kalman Filter, which is used to comprise the inter-frame predication and single-frame measurement of features' discriminative power. In this way, we extend the traditional filter framework from modeling motion states to modeling feature evaluation. Experiments show this method can greatly improve the tracking stabilization when objects go across complex backgrounds.
Year
DOI
Venue
2008
10.1109/ICPR.2008.4761152
19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6
Keywords
Field
DocType
color histogram,object tracking,kalman filters,tracking,feature extraction,pixel,histograms,color,kalman filter
Histogram,Object detection,Computer vision,Color histogram,Pattern recognition,Computer science,Feature extraction,Kalman filter,Video tracking,Artificial intelligence,Pixel,Discriminative model
Conference
ISSN
Citations 
PageRank 
1051-4651
15
0.93
References 
Authors
5
3
Name
Order
Citations
PageRank
Zhenjun Han117616.40
Qixiang Ye291364.51
Jianbin Jiao336732.61