Online feature evaluation for object tracking using Kalman Filter

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.

[1]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[2]  Datong Chen,et al.  Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Wen Gao,et al.  Online Selection of Discriminative Features Using Bayes Error Rate for Visual Tracking , 2006, PCM.

[4]  Yanxi Liu,et al.  Online selection of discriminative tracking features , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Wen Gao,et al.  Online selecting discriminative tracking features using particle filter , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Raúl Rojas,et al.  Kalman filter for vision tracking , 2005 .