Proc. SPIE. 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018)
KEYWORDS: Genetic algorithms, Visual process modeling, Video acceleration, Detection and tracking algorithms, Visualization, Imaging systems, Video, Parallel computing, Information visualization, RGB color model
Template matching for image sequences captured by mobile camera is widely applied in machine vision, RTLS (Real Time Location System), ADAS (Advanced Driver Assistant Systems), ITS (Intelligent Transportation System) and video surveillance system. Nowadays, the target tracking algorithms are mainly divided into two categories: generative model method and discriminative model method. Currently, discriminative model method is popular. This method mainly adopts image feature combined with machine learning to achieve template matching. Such algorithms require adequate image samples and tedious calculations, so there are many difficulties in the application of on-board systems such as ADAS and ITS. In this paper, we present a method based on visual feature information and structure information which can improve the accuracy of template matching effectively and proposed cuda architecture based parallel acceleration algorithm. Compared with previous method, the proposed method can achieve template matching robustly while maintaining a short operation time, so that it can be easily ported to the vehicle-mounted system.