Abstract
In this paper an improved Mean-shift tracking algorithm based on adaptive multiple feature fusion is presented. A two-class variance ratio is employed to measure the discriminate between object and background. The multiple features are Fused by linear weighting to realise Mean-shift tracking using the discrimination. Furthermore, an adaptive model updating mechanism based on the likelihood of the features between successive frames is addressed to alleviate the mode drifts. Based on biology vision theory, colour, edge and texture cue are employed to implement the scheme. Experiments on several video sequences show the effectiveness of the proposed method.
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Yin, H., Chai, Y., Yang, S.X., Chiu, D.K.Y. (2011). An Improved Mean-shift Tracking Algorithm Based on Adaptive Multiple Feature Fusion. In: Cetto, J.A., Filipe, J., Ferrier, JL. (eds) Informatics in Control Automation and Robotics. Lecture Notes in Electrical Engineering, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19730-7_4
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DOI: https://doi.org/10.1007/978-3-642-19730-7_4
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