Abstract
Compressive sensing (Compressed sensing, CS) is a novel sampling technique which adopts reconstruction algorithms to reconstruct original signals from significantly fewer samples than those using the Nyquist-Shannon sampling theorem. Recently, several researches have been conducted to apply the CS framework to various applications. In this thesis, we propose improved reconstruction methods based on CS for object recognition and tracking. In recent years, a sparse representation-based classification (SRC) method based on CS is presented for robust face recognition. Our first proposed enhancement is adopting a maximum probability of the partial ranking method based on the framework of SRC, called SRC-MP. It computes the maximum probability from the largest γ weighting coefficients for the subjects. The criterion of selection is now based on the maximum probability, instead of the largest weighting coefficients. Experiments are implemented on face and real-world fish databases. Experimental results show that our proposed method is able to achieve higher accuracy than projection-based methods, such as principal component analysis (PCA), linear discriminant analysis (LDA), 2DPCA and 2DLDA, and matching pursuit related algorithms, such as orthogonal matching pursuit (OMP), compressive sampling matching pursuit (CoSaMP), subspace pursuit (SP), and regularized OMP (ROMP). On the other hand, a real-time compressive tracking (CT) method based on CS is proposed for object tracking. Our proposed enhancement is implemented for a sparse sample collection and representation (SSCR) method, based on CT and SRC concepts, for real-world fish tracking. The SSCR consists of sample collection and sparse sample representation procedures. The sample collection procedure incorporates background subtraction into CT to improve the accuracy of collecting sets of three kinds of samples (positive, negative, and predictive). The sparse sample representation procedure represents each predictive sample as a sparse linear combination of all positive and negative samples. The weights of the predictive samples are computed using our proposed re-weighting and dynamically updating orthogonal matching pursuit (RwDuOMP) method. The RwDuOMP method includes three procedures, picking over samples, re-weighting the picked samples, and dynamically updating negative samples. The predictive sample with the maximum weighting coefficient is regarded as the target object tracking result. We evaluate the SSCR method using several challenging real-world underwater sequences from an uncontrolled open sea in Taiwan. In addition, we compare the RwDuOMP method with OMP, CoSaMP, SP and ROMP methods. Experimental results indicate that our proposed method improves the accuracy of fish tracking.