access icon free Multi-band joint local sparse tracking via wavelet transforms

© The Institution of Engineering and Technology
Received 08/03/2016, Accepted 21/06/2016, Revised 30/05/2016, Published 24/06/2016

A novel multi-band joint local sparse tracking algorithm via wavelet transforms is proposed in this study. The object image may contain rich information of different types; the authors use wavelet transforms to decompose the object image into some sub-band images first. This will help extract the information in different frequency ranges for the object. Then same block operation is executed on all the sub-band images. The l 2, 1 mixed-norm is used to describe the multi-band joint local sparse representation on each patch; it can effectively extract the structural information in different frequency ranges. Thus, more accurate object appearance model can be established. Second, the coefficients on the diagonal of coefficient matrix are extracted for the confidence degrees of the candidate objects in this band, and then the confidence degree results in all the bands are fused to determine the best candidate object in the current frame. This can effectively alleviate the object drifting. Finally, both qualitative and quantitative evaluation results on 15 challenging video sequences demonstrate that the proposed tracking algorithm in this study can achieve better tracking effects compared with the other state-of-the-art algorithms.

Inspec keywords: matrix algebra; image representation; image sequences; feature extraction; image fusion; wavelet transforms; video signal processing; object tracking

Other keywords: diagonal of coefficient matrix; object image deomposition; wavelet transforms; sub-band images; object drifting; object appearance model; structural information extraction; multiband joint local sparse representation; multiband joint local sparse tracking algorithm; video sequences; l2,1 mixed-norm

Subjects: Image recognition; Algebra; Algebra; Computer vision and image processing techniques; Integral transforms; Integral transforms; Video signal processing; Sensor fusion

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