Recover keypoint-based target tracking from occlusion using deep neural network segmentation

摘要

An approach is provided that captures a set of sequential images of an area where there is a selected moving object. Both a keypoint-based (KP-based) matching model and a neural network based (NN-based) matching model are used with the KP-based matching model analyzing most or all of the captured images and the NN-based model being more computational intensive and analyzing a subset of the images. When the KP-based matching model fails to identify the selected object in an image, the NN-based model is used to find the object so that the KP-based matching model can re-establish tracking of the object.