Abstract
Recently, hand poses estimation is a very popular method in human-computer interface, virtual reality. In this thesis, we introduce a model-based method: to search appropriate parameter in 3-D hand model by Particle Swarm Optimization. To improve this algorithm, we combine difference function as distance and apply LHS(Locality-Sensitive Hashing) as that PSO can run in a discrete hand model successfully. The difference function, which is used to evaluate the similarity of the samples in hand model and input image’s depth distribution. Using PSO for the discrete hand model, we compute the smallest distance between hand model and the new estimate parameter in each iteration. To reduce this compute, we use LHS, searching in the look-up table. Combining PSO, difference function and LHS, our system and achieve in real-time successfully. First, we obtain the hand silhouette using depth map. Because of depth map, it is insensitive in light change. Then we have depth distribution from kinect sensor and 16-D hand parameter by using 5DT data glove as a sample. With many samples in a 3-D hand model, we may search the model by using LHS. Based on the 3-D hand model construction, we estimate the hand pose in input image. In testing process, with the depth distribute of the hand, we use difference function to evaluate distance between model and input image, and PSO should estimate parameter by many particles and iterations. By control the number of the particle and iteration, we can achieve the estimate system run in real-time.