Pedestrian target tracking based on the UAV platform can be widely used in traffic control, field search, and military reconnaissance. It is an important research task of computer vision and intelligent cruise. Aiming at the limitations of the UAV surveillance system in moving pedestrian target tracking, such as background change, pedestrian deformation, occlusion interference, and lack of real-time performance, the dual Kalman filter is used to improve the traditional TLD tracking algorithm, the proposed method can accelerate the correction of the predicted detection area, reduce the disturbance of the environment background and the target deformation to the pedestrian tracking accuracy, and reduce the detection time by using the adaptive adjustment method of the detection area to offset the time cost caused by double Kalman filtering, to improve the Algorithm’s real-time performance. The test results show that the proposed method has high accuracy, stability, and real-time performance in pedestrian target tracking based on the UAV platform.
Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system’s image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.
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