Object tracking using combination of daubechies complex wavelet transform and Zernike moment

Abstract

Moving object tracking is one of the challenging problems in computer vision and it has numerous applications in surveillance system, traffic monitoring etc. The goal of object tracking algorithm is to locate a moving object in consecutive video frames. Tracking of moving object in a video becomes difficult due to random motion of objects. This paper introduces a new algorithm for moving object tracking by exploiting the properties of Daubechies complex wavelet transform and Zernike moment. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of object. The motivation behind using combination of these two as a feature of object, because shift invariance and better edge representation property make Daubechies complex wavelet transform suitable for locating object in consecutive frames whereas rotation invariance properties of Zernike moment is also helpful for correct object identification in consecutive frames. Therefore combination of these two as feature of object gives better results. The proposed method matches Zernike moments of Daubechies complex wavelet coefficients of object in the first frame to next consecutive frames. The experimental results and performance evaluation parameters validate that the proposed method gives better performance against other state-of-the-art methods.

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