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Conference Publication

Dynamic Compressive Tracking

Host Publication: International Conference on Advances in Mobile Computing & Multimedia

Authors: T. Chen, Y. Zhang, T. Yang and H. Sahli

Publisher: ACM

Publication Year: 2013

Number of Pages: 7


Abstract:

The original real-time compressive tracking use a random matrix to get the appearance model based on features extracted form the image feature space performs well in most scene. However, when the object is low-grain, low-resolution, or small, a fixed size sparse measurement matrix is not sufficient enough to preserve the structure of the image of the object. In this work, we propose a Dynamic Compressive Tracking algorithm that employs adaptive random projections that preserve the structure of the image feature space of objects during tracking. The proposed tracker automatically estimates and ranks the amount of random feature projections of the object in the compressive domain. Extensive experimental results, on challenging public available data sets shows, that the proposed dynamic compressible tracking algorithm outperforms conventional compressive tracker, and it is comparable to the state-of-the-art tracking methods.

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Prof. Hichem Sahli

+32 (0)02 629 291

hsahli@etrovub.be

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