Research of a real-time feature point tracking method based on the combination of improved SURF and P-KLT algorithm

In order to track moving targets of image sequences in real time, a novel feature point tracking algorithm is proposed by using a combination of improved speeded up robust features (SURF) algorithm and pyramid kanade-lucas-tomasi (P-KLT) matching algorithm. First, the target box is marked manually and improved SURF algorithm is applied to extract features which are robust and distinctive in different blocks. Then, the features are tracked stably by the hierarchically iterative matching of P-KLT algorithm and mismatched points are eliminated utilizing a statistical method. Finally, the exact location of the target is obtained with the application of extracting the target contour by the Greedy Snake algorithm and the target box is updated automatically. Furthermore, discrete feature filter and adaptive feature updating strategy are designed to improve the robustness. Simulation results show that the algorithm can adapt to objective changes in attitude and size and track stably aerial vehicles with simple structures. Time consumption per frame is 31.8 ms, which is 47.1% less than SIFT+P-KLT algorithm. Geometric center error per frame is 5.03 pixel, which is 27.2% less than GFTT+P-KLT tracking algorithm and 38.6% less than SIFT+P-KLT tracking algorithm. Contour area error per frame is 16.3%, which is 56.9% less than GFTT+P-KLT tracking algorithm and 68.4% less than SIFT+P-KLT tracking algorithm.