A hybrid motion and appearance prediction model for robust visual object tracking

Authors:
Hamidreza Jahandide;Kamal Mohamedpour;Hamid Abrishami Moghaddam
Affiliations:
K.N. Toosi University of Technology, Seyed Khandan, P.O. Box 16315-1355, Tehran, Iran;K.N. Toosi University of Technology, Seyed Khandan, P.O. Box 16315-1355, Tehran, Iran;K.N. Toosi University of Technology, Seyed Khandan, P.O. Box 16315-1355, Tehran, Iran
Venue:
Pattern Recognition Letters
Year:
2012

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Abstract

In this paper a new video object tracking method is proposed. A hybrid model based on motion and appearance is constructed for the object and Kalman filter is applied to both components in order to reduce noise and provide a prediction for the next frame. Making available a prediction of the object appearance in the next frame contributes effectively in robust object tracking in spite of large changes in scene illumination. Experimental results using the proposed method and its counterparts without appearance prediction demonstrate the superiority of the novel hybrid prediction method under drastic changes in illumination.