Real-time tracking of deformable objects based on combined matching-and-tracking

Abstract

Visual tracking is very challenging due to the existence of several sources of variations, such as partial occlusion, deformation, scale variation, rotation, and background clutter. A model-free tracking method based on fusing accelerated features using fast explicit diffusion in nonlinear scale spaces (AKAZE) and KLT features is presented. First, matching-keypoints are generated by finding corresponding keypoints from the consecutive frames and the object template, then tracking-keypoints are generated using the forward–backward flow tracking method, and at last, credible keypoints are obtained by AKAZE-KLT tracking (AKT) algorithm. To avoid the instability of a statistical method, the median method is adopted to compute the object's location, scale, and rotation in each frame. The experimental results show that the AKT algorithm has strong robustness and can achieve accurate tracking especially under conditions of partial occlusion, scale variation, rotation, and deformation. The tracking performance shows higher robustness and accuracy in a variety of datasets and the average frame rate reaches 78 fps, showing good performance in real time.