Computer vision based interaction between bare hands and virtual objects is an urgent problem to be solved in augmented reality and teleoperation. Bare hand tracking is one of the key issues. An effective hand tracking method based on compressed sensing and multiple feature descriptors is studied in depth. Firstly, a rectangular tracking window containing the hand is determined manually in the initial frame. Using the compressed sensing theory, key Haar feature values and HOG (abbreviation of histogram of oriented gradients) feature values of the initial tracking window are calculated respectively. Thus the classifier is initialized. For the subsequent frames, those positive samples and negative ones around the moving hand are captured, their feature values are calculated, and the classifier is updated. The candidate region corresponding to the maximum of the classifier is taken as the target region of the moving hand in each frame. In the process, Haar feature values and HOG feature values of the candidate region samples are calculated respectively. Simulation experiments and real experiments are carried out by using the proposed tracking method. Experimental results demonstrate that the proposed method can track the moving hand effectively. The proposed hand tracking method can be used in the fields of human computer interaction, augmented reality and teleoperation.
Multi-sensor image fusion has its effective utilization for surveillance and navigation. It provides a way to merge multisensor
imagery by combining the outputs of different imaging sensors. In this paper, we utilize a variational approach to
fuse images from different sensors, in order to enhance visualization for surveillance. Energy functional is established in
a contrast vector field and a successive over-relaxation method is utilized to solve a Poisson equation. Experimental
results show that the variational approach is robust and effective, regardless of time-consuming.