Automatic, Real Time, Unsupervised Spatio-temporal 3D Object Detection Using RGB-D Cameras

Abstract

The evolution and affordability of depth cameras like Microsoft Kinect make it a great source for object detection and surveillance monitoring. Information available from depth cameras includes depth in addition to color. Using depth cameras, the provided depth information can be incorporated for object detection in still and video images, but needs special care to pair it with color information. In this work, we propose a simple, yet novel real time unsupervised object detection method in spatio-temporal videos. The RGB color frame is mapped into Hunter Lab color space to reduce emphasis on image illuminations, while the depth frame is back-projected into the 3D real world coordinate in order to distinguish between objects in space. Once combined, the mapped color information and the back-projected depth information are fed into automatic, unsupervised clustering framework in order to detect scene objects. The framework runs in parallel to provide real time spatio-temporal object detection.