Motion segmentation is part of the computer vision field and aims to find the moving parts in a video sequence. It is used in applications such as autonomous driving, surveillance, robotics, human motion analysis, and video indexing. Since there are so many applications, motion segmentation is ill-defined and the research field is vast. Despite the advances in the research over the years, the existing methods are still far behind human capabilities. Problems such as changes in illumination, camera motion, noise, mixtures of motion, missing data, and occlusion remain challenges. Feature-based approaches have grown in popularity over the years, especially manifold clustering methods due to their strong mathematical foundation. Methods exploiting sparse and low-rank representations are often used since the dimensionality of the data is reduced while useful information regarding the motion segments is extracted. However, these methods are unable to effectively handle large and complete occlusions as well as missing data since they tend to fail when the amount of missing data becomes too large. An algorithm based on Sparse Subspace Clustering (SSC) has been proposed to address the issue of occlusions and missing data so that SSC can handle these cases with high accuracy. A frame-to-frame analysis was adopted as a pre-processing step to identify motion segments between consecutive frames, called inter-frame motion segments. The pre-processing step is called Multiple Split-And-Merge (MSAM), which is based on the classic top-down split-and-merge algorithm. Only points present in both frame pairs are segmented. This means that a point undergoing an occlusion is only assigned to a motion class when it has been visible for two consecutive frames after re-entering the camera view. Once all the inter-frame segments have been extracted, the results are combined in a single matrix and used as the input for the classic SSC algorithm. Therefore, SSC segments inter-frame motion segments rather than point trajectories. The resulting algorithm is referred to as MSAM-SSC. MSAM-SSC outperformed some of the most popular manifold clustering methods on the Hopkins155 and KT3DMoSeg datasets. It was also able to handle complete occlusions and 50% missing data sequences, as well as outliers. The algorithm can handle mixtures of motions and different numbers of motions. However, it was found that MSAM-SSC is more suited for traffic and articulate motion scenes which are often used in applications such as robotics, surveillance, and autonomous driving. For future work, the algorithm can be optimised to reduce the execution time so that it can be used for real-time applications. Additionally, the number of moving objects in the scene can be estimated to obtain a method that does not rely on prior knowledge. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021. / CSIR / Electrical, Electronic and Computer Engineering / MEng (Computer Engineering) / Unrestricted

In this dissertation, some new algorithms are developed for hyperspectral imaging analysis enhancement. Tensor data format is applied in hyperspectral dataset sparse and low-rank decomposition, which could enhance the classification and detection performance. And multi-view learning technique is applied in hyperspectral imaging clustering. Furthermore, kernel version of multi-view learning technique has been proposed, which could improve clustering performance. Most of low-rank and sparse decomposition algorithms are based on matrix data format for HSI analysis. As HSI contains high spectral dimensions, tensor based extended low-rank and sparse decomposition (TELRSD) is proposed in this dissertation for better performance of HSI classification with low-rank tensor part, and HSI detection with sparse tensor part. With this tensor based method, HSI is processed in 3D data format, and information between spectral bands and pixels maintain integrated during decomposition process. This proposed algorithm is compared with other state-of-art methods. And the experiment results show that TELRSD has the best performance among all those comparison algorithms. HSI clustering is an unsupervised task, which aims to group pixels into different groups without labeled information. Low-rank sparse subspace clustering (LRSSC) is the most popular algorithms for this clustering task. The spatial-spectral based multi-view low-rank sparse subspace clustering (SSMLC) algorithms is proposed in this dissertation, which extended LRSSC with multi-view learning technique. In this algorithm, spectral and spatial views are created to generate multi-view dataset of HSI, where spectral partition, morphological component analysis (MCA) and principle component analysis (PCA) are applied to create others views. Furthermore, kernel version of SSMLC (k-SSMLC) also has been investigated. The performance of SSMLC and k-SSMLC are compared with sparse subspace clustering (SSC), low-rank sparse subspace clustering (LRSSC), and spectral-spatial sparse subspace clustering (S4C). It has shown that SSMLC could improve the performance of LRSSC, and k-SSMLC has the best performance. The spectral clustering has been proved that it equivalent to non-negative matrix factorization (NMF) problem. In this case, NMF could be applied to the clustering problem. In order to include local and nonlinear features in data source, orthogonal NMF (ONMF), graph-regularized NMF (GNMF) and kernel NMF (k-NMF) has been proposed for better clustering performance. The non-linear orthogonal graph NMF combine both kernel, orthogonal and graph constraints in NMF (k-OGNMF), which push up the clustering performance further. In the HSI domain, kernel multi-view based orthogonal graph NMF (k-MOGNMF) is applied for subspace clustering, where k-OGNMF is extended with multi-view algorithm, and it has better performance and computation efficiency.