A fast visual tracking method via spatio-temporal context learning for unmanned rotorcrafts fixed-pointed landing

In this paper, we propose a visual tracking method based on spatio-temporal context model, to solve the fast and stable target tracking problem with large scale changes, which is suitable for unmanned rotorcrafts high precision fixed-pointed landing using vision-based navigation. Based on the Bayesian outline, we express the spatio-temporal connections between the interest object and local context. The local context represents the statistical correlation between the target features and its adjacent areas. Then, by integrating this spatio-temporal context model and focusing properties of biological vision system, we compute a confidence map and obtain the best target location by maximizing an object location likelihood function. This paper presents a rapid model learning and object detection technique via Fast Fourier Transform (FFT), and proposes a multiscale estimation method to resolve the large scale changes problem during fix-pointed landing. Experimental results show that the proposed algorithm for visual tracking has respectable performance during partial occlusion, scale changes, lighting changes, deformation, motion blur and background clutter. In terms of efficiency, accuracy and robustness, our method meets the requirements of embedded image processing system in UAVs.