On feature combination and multiple kernel learning for object tracking

Authors:
Huchuan Lu;Wenling Zhang;Yen-Wei Chen
Affiliations:
School of Information and Communication Engineering, Dalian University of Technology, Dalian, China;School of Information and Communication Engineering, Dalian University of Technology, Dalian, China;College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan
Venue:
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Year:
2010

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Abstract

This paper presents a new method for object tracking based on multiple kernel learning (MKL). MKL is used to learn an optimal combination of χ2 kernels and Gaussian kernels, each type of which captures a different feature. Our features include the color information and spatial pyramid histogram (SPH) based on global spatial correspondence of the geometric distribution of visual words. We propose a simple effective way for on-line updating MKL classifier, where useful tracking objects are automatically selected as support vectors. The algorithm handle target appearance variation, and makes better usage of history information, which leads to better discrimination of target and the surrounding background. The experiments on real world sequences demonstrate that our method can track objects accurately and robustly especially under partial occlusion and large appearance change.