An integrated system for object tracking, detection, and online learning with real-time RGB-D video
This paper introduces a highly integrated system providing very accurate object detection with RGB-D sensor. To solve the problem that there are always insufficient training sets for object detection in real world, we present an online learning architecture to learn templates and to detect objects real-time. The proposed novel concept skips the training phase required in previous recognition works, and it comprises independent tracking and detection function, which collaborates with each other to make the detection more precise. We furthermore illustrate four strategies for online learning and compare the efficiency. With depth information, the experiment results perform remarkable in challenging scenarios.