Framework for Object Tracking with Support Vector Machines, Structural Tensor and the Mean Shift Method

Authors:
Bogusław Cyganek
Affiliations:
AGH University of Science and Technology, Kraków, Poland 30-059
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Year:
2009

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Pixel-Based object detection and tracking with ensemble of support vector machines and extended structural tensor

ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I

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Abstract

In this paper a system is presented for object tracking based on the novel connection of the one-class SVM classifier with the mean shift tracker. An object for tracking is defined by feature vectors composed of the components of the orthogonal color space, as well as local phase and coherence components of the structural tensor which convey information on texture. The binary output of the SVM is mapped into a membership field with a proposed transformation function. Tracking is performed with the continuously adaptive mean shift method operating in the membership field. The method shows high discriminative power and fast run-time properties.