The fundamental task in human motion analysis is the extraction or capture of human motion and the established industrial technique is marker-based human motion capture. However, marker-based systems, apart from being expensive, are obtrusive and require a complex, time-consuming experimental setup, resulting in increased user discomfort. As an alternative solution, research on markerless human motion analysis has increased in prominence. In this thesis, we present three human motion analysis algorithms performing markerless tracking and classification from multiple-view studio-based video sequences using particle swarm optimisation and charting, a subspace learning technique.In our first framework, we formulate, and perform, human motion tracking as a multi-dimensional non-linear optimisation problem, solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm. PSO initialises automatically, does not need a sequence-specific motion model, functioning as a blackbox system, and recovers from tracking divergence through the use of a hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter, annealed particle filter and partitioned sampling annealed particle filter, and report similar or better tracking performance. Additionally we report an extensive experimental study of HPSO over ranges of values of its parameters and propose an automatic-adaptive extension of HPSO called as adaptive particle swarm optimisation. Next, in line with recent interest in subspace tracking, where low-dimensional subspaces are learnt from motion models of actions, we perform tracking in a low-dimensional subspace obtained by learning motion models of common actions using charting, a nonlinear dimensionality reduction tool. Tracking takes place in the subspace using an efficient modified version of particle swarm optimisation. Moreover, we perform a fast and efficient pose evaluation by representing the observed image data, multi-view silhouettes, using vector-quantized shape contexts and learning the mapping from the action subspace to shape space using multi-variate relevance vector machines. Tracking results with various action sequences demonstrate the good accuracy and performance of our approach.Finally, we propose a human motion classification algorithm, using charting-based low-dimensional subspaces, to classify human action sub-sequences of varying lengths, or snippets of poses. Each query action is mapped to a single subspace space, learnt from multiple actions. Furthermore we present a system in which, instead of mapping multiple actions to a single subspace, each action is mapped separately to its action-specific subspace. We adopt a multi-layered subspace classification scheme with layered pruning and search. One of the search layers involves comparing the input snippet with a sequence of key-poses extracted from the subspace. Finally, we identify the minimum length of action snippet, of skeletal features, required for classification, using competing classification systems as the baseline. We test our classification component on HumanEva and CMU mocap datasets, achieving similar or better classification accuracy than various comparable systems. human motion and the established industrial technique is marker-based human motion capture. However, marker-based systems, apart from being expensive, are obtrusive and require a complex, time-consuming experimental setup, resulting in increased user discomfort. As an alternative solution, research on markerless human motion analysis has increased in prominence. In this thesis, we present three human motion analysis algorithms performing markerless tracking and clas- si?cation from multiple-view studio-based video sequences using particle swarm optimisation and charting, a subspace learning technique. In our ?rst framework, we formulate, and perform, human motion tracking as a multi-dimensional non-linear optimisation problem, solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm. PSO initialises automat- ically, does not need a sequence-speci?c motion model, functioning as a black- box system, and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experiment- ally HPSO with particle ?lter, annealed particle ?lter and partitioned sampling annealed particle ?lter, and report similar or better tracking performance. Addi- tionally we report an extensive experimental study of HPSO over ranges of values of its parameters and propose an automatic-adaptive extension of HPSO called as adaptive particle swarm optimisation. Next, in line with recent interest in subspace tracking, where low-dimensional subspaces are learnt from motion models of actions, we perform tracking in a low-dimensional subspace obtained by learning motion models of common actions using charting, a nonlinear dimensionality reduction tool. Tracking takes place in the subspace using an e?cient modi?ed version of particle swarm optimisa- tion. Moreover, we perform a fast and e?cient pose evaluation by representing the observed image data, multi-view silhouettes, using vector-quantized shape contexts and learning the mapping from the action subspace to shape space us- ing multi-variate relevance vector machines. Tracking results with various action sequences demonstrate the good accuracy and performance of our approach. Finally, we propose a human motion classi?cation algorithm, using charting-based low-dimensional subspaces, to classify human action sub-sequences of varying lengths, or snippets of poses. Each query action is mapped to a single subspace space, learnt from multiple actions. Furthermore we present a system in which, instead of mapping multiple actions to a single subspace, each action is mapped separately to its action-speci?c subspace. We adopt a multi-layered subspace classi?cation scheme with layered pruning and search. One of the search lay- ers involves comparing the input snippet with a sequence of key-poses extracted from the subspace. Finally, we identify the minimum length of action snippet, of skeletal features, required for accurate classi?cation, using competing classi?ca- tion systems as the baseline. We test our classi?cation component on HumanEva and CMU mocap datasets, achieving similar or better classi?cation accuracy than

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.

Face and gait recognition problems are challenging due to largely varying appearances, highly complex pattern distributions, and insufficient training samples. This dissertation focuses on multilinear subspace learning for face and gait recognition, where low-dimensional representations are learned directly from tensorial face or gait objects. This research introduces a unifying multilinear subspace learning framework for systematic treatment of the multilinear subspace learning problem. Three multilinear projections are categorized according to the input-output space mapping as: vector-to-vector projection, tensor-to-tensor projection, and tensor-to-vector projection. Techniques for subspace learning from tensorial data are then proposed and analyzed. Multilinear principal component analysis (MPCA) seeks a tensor-to-tensor projection that maximizes the variation captured in the projected space, and it is further combined with linear discriminant analysis and boosting for better recognition performance. Uncorrelated MPCA (UMPCA) solves for a tensor-to-vector projection that maximizes the captured variation in the projected space while enforcing the zero-correlation constraint. Uncorrelated multilinear discriminant analysis (UMLDA) aims to produce uncorrelated features through a tensor-to-vector projection that maximizes a ratio of the between-class scatter over the within-class scatter defined in the projected space. Regularization and aggregation are incorporated in the UMLDA solution for enhanced performance. Experimental studies and comparative evaluations are presented and analyzed on the PIE and FERET face databases, and the USF gait database. The results indicate that the MPCA-based solution has achieved the best overall performance in various learning scenarios, the UMLDA-based solution has produced the most stable and competitive results with the same parameter setting, and the UMPCA algorithm is effective in unsupervised learning in low-dimensional subspace. Besides advancing the state-of-the-art of multilinear subspace learning for face and gait recognition, this dissertation also has potential impact in both the development of new multilinear subspace learning algorithms and other applications involving tensor objects.