The Microsoft Kinect is part of a wave of new sensing technologies. Its RGB-D camera is capable of providing high quality synchronized video of both color and depth data. Compared to traditional 3-D tracking techniques that use two separate RGB cameras’ images to calculate depth data, the Kinect is able to produce more robust and reliable results in object recognition and motion tracking. Also, due to its low cost, the Kinect provides more opportunities for use in many areas compared to traditional more expensive 3-D scanners. In order to use the Kinect as a range sensor, algorithms must be designed to first recognize objects of interest and then track their motions. Although a large number of algorithms for both 2-D and 3-D object detection have been published, reliable and efficient algorithms for 3-D object motion tracking are rare, especially using Kinect as a range sensor.
In this paper, algorithms for object recognition and tracking that can make use of both RGB and depth data in different scenarios are introduced. Subsequently, efficient methods for scene segmentation including background and noise filtering are discussed. Taking advantage of those two kinds of methods, a prototype system that is capable of working efficiently and stably in various applications related to educational laboratories is presented.