PFBIK-tracking: Particle filter with bio-inspired keypoints tracking

In this paper, we propose a robust detection and tracking method for 3D objects by using keypoint information in a particle filter. Our method consists of three distinct steps: Segmentation, Tracking Initialization and Tracking. The segmentation is made in order to remove all the background information, in order to reduce the number of points for further processing. In the initialization, we use a keypoint detector with biological inspiration. The information of the object that we want to follow is given by the extracted keypoints. The particle filter does the tracking of the keypoints, so with that we can predict where the keypoints will be in the next frame. In a recognition system, one of the problems is the computational cost of keypoint detectors with this we intend to solve this problem. The experiments with PFBIK-Tracking method are done indoors in an office/home environment, where personal robots are expected to operate. The Tracking Error evaluate the stability of the general tracking method. We also quantitatively evaluate this method using a “Tracking Error”. Our evaluation is done by the computation of the keypoint and particle centroid. Comparing our system with the tracking method which exists in the Point Cloud Library, we archive better results, with a much smaller number of points and computational time. Our method is faster and more robust to occlusion when compared to the OpenniTracker.