Adaptive kernel based tracking using mean-shift

Authors:
Jie-Xin Pu;Ning-Song Peng
Affiliations:
Electronic Information Engineering College, Henan University of Science and Technology, Luoyang, China;Electronic Information Engineering College, Henan University of Science and Technology, Luoyang, China
Venue:
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Year:
2006

Citing 2
Cited 0

Mean Shift, Mode Seeking, and Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

The mean shift algorithm is an kernel based way for efficient object tracking. However, there is presently no clean mechanism for selecting kernel bandwidth when the object size is changing. We present an adaptive kernel bandwidth selection method for rigid object tracking. The kernel bandwidth is updated by using the object affine model that is estimated by using object corner correspondences between two consecutive frames. The centroid of object is registered by a special backward tracking method. M-estimate method is used to reject mismatched pairs (outliers) so as to get better regression results. We have applied the proposed method to track vehicles changing in size with encouraging results.