Kinect Sensor based Object Feature Estimation in Depth Images

Kinect is a motion-sensing device which was originally developed for the Xbox 360 gaming console. This recently developed low-cost sensor detects the body position, motion, and voice; it consists of a microphone, a RGB camera, and a depth sensor. Kinect is PC-centric sensor which allows developers to develop real-life applications with human gestures and body motions. This paper presents an approach to interpret the indoor room objects in order to match the objects features in depth images captured from an RGBD video database. The dataset consists of color and depth image pairs gathered in real-time indoor home environment. The objects features are matched in depth image pairs with the feature association method to detect stable features at different time instances.

[1]  Jake K. Aggarwal,et al.  Human detection using depth information by Kinect , 2011, CVPR 2011 WORKSHOPS.

[2]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[3]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[4]  Dieter Fox,et al.  RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments , 2010, ISER.

[5]  Max Mignotte,et al.  Fall Detection from Depth Map Video Sequences , 2011, ICOST.

[6]  Martin D. Levine,et al.  Registering Multiview Range Data to Create 3D Computer Objects , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Dieter Fox,et al.  RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments , 2012, Int. J. Robotics Res..

[8]  Dieter Fox,et al.  A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.

[9]  Derek Hoiem,et al.  Indoor Segmentation and Support Inference from RGBD Images , 2012, ECCV.

[10]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Vangelis Metsis,et al.  A viewpoint-independent statistical method for fall detection , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[12]  W. Burgard,et al.  Real-time 3 D visual SLAM with a hand-held RGB-D camera , 2011 .

[13]  Dieter Fox,et al.  Sparse distance learning for object recognition combining RGB and depth information , 2011, 2011 IEEE International Conference on Robotics and Automation.

[14]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.