Efficient tracking of the 3D articulated motion of human hands

The problem of hand pose estimation and tracking is both theoretically and practically interesting. It is a challenging problem that hasn’t been solved in its full generality despite the significant amount of effort that has been devoted to it. This thesis presents methods to track the position, orientation and full articulation of human hands in various everyday scenarios. Investigated scenarios include tracking one or two hands and tracking the hand(s) in isolation or in interaction with the environment. Design choices for the various presented methods regard the type of input, the selection of appropriate visual cues and furthermore the way they are synthesized and evaluated, as well as the optimization algorithms used to solve the formulated optimization problems. All scenarios use markerless visual observations of the scene as input. We explore the visual cues of skin color, edges, depth map, and visual hull. These observations can come either from a network of cameras or from an RGB-D sensor. The choice of input type partially mandates the visual cues that are employed. We follow a model-based approach to the problem, formulating the pose estimation task for each frame as an optimization problem. The search space of this problem uses the adopted representation for the hand kinematics. For the case of single hand, the search space is this set of kinematics parameters, whereas for handobject or hand-hand interaction, this search space is appropriately augmented to include all the tracked entities. This joint consideration, while resulting in optimization problems with tens of parameters, has the advantage that the interaction between the tracked objects can be effortlessly modeled and evaluated. The temporal continuity assumption is used by initializing the search for a frame near the solution for the previous frame. Joint modeling of the observed entities in the scene allows for effortlessly treating scenarios of complex interaction between these entities. For the case of hand-object interaction, we show how the observed occlusions can provide useful information instead of being an obstacle. For the case of two hands in strong interaction, to the best of our knowledge, the presented results involve the most complex hand-hand interaction attempted so far in the relevant literature. For the task of optimizing the objective functions that result from the adopted formulation of the problem, we use black-box optimization algorithms. Specifically, variants of Particle Swarm Optimization (PSO) are employed in most scenarios. PSO is an evolutionary optimization algorithm that is derivative-free and easily parallelizable. It is suitable for our task, since it is well-suited to multi-modal, nondifferentiable objective functions. A novel evolutionary optimization algorithm is also presented in this thesis, and applied to two of the examined scenarios. This algorithm exploits the useful properties of quasi-random sampling, as well as the power of evolutionary computing. The various computational steps of all presented methods are carefully designed so that they include parallelizable computations. It is then possible to make use of modern hardware such as the GPU architecture, resulting in practical systems that achieve real-time or interactive frame-rates.