Javier Vargas
ABSTRACT
The present paper describes a study based on the loss and gain of frames that are obtained through sensors (color, depth, and body tracking) of Kinect V2. For this purpose, it is established a time to obtain each frames per second (FPS), evaluating a performance of sensors in three evaluation instances, using native Kinect V2 libraries and other graphic processing libraries. In addition, several experimental tests were carried out, in order to verify the best performance of a test application based on graphical processing of each sensor.
- Kinect for Xbox One | Xbox: http://www.xbox.com/en-US/xbox-one/accessories/kinect.Google Scholar
- Kinect - Windows app development: https://developer.microsoft.com/en-us/windows/kinect.Google Scholar
- Kinect hardware: https://developer.microsoft.com/en-us/windows/kinect/hardware.Google Scholar
- Kinect hardware setup: https://developer.microsoft.com/en-us/windows/kinect/hardware-setup.Google Scholar
- Mokhov, S.A., Song, M., Llewellyn, J., Zhang, J., Charette, A., Wu, R. and Ge, S. 2016. Real-time collection and analysis of 3-kinect v2 skeleton data in a single application. (Jul. 2016).Google Scholar
- Córdova-Esparza, D.M., Terven, J.R., Jiménez-Hernández, H. and Herrera-Navarro, A.M. 2017. A multiple camera calibration and point cloud fusion tool for Kinect V2. 143, (Sep. 2017), 1--8.Google Scholar
- Gao, T.S., Sheng, D.B., Nguyen, T.H., Jeong, N.S., Kim, H.K. and Kim, S.B. 2017. Measurement of the fish body wound depth based on a depth map inpainting method. (2017), 289--299.Google Scholar
- Kim, C., Yun, S., Jung, S.W. and Won, C.S. 2016. Color and depth image Correspondence for Kinect v2. (2016), 333--340.Google Scholar
- Yang, L., Zhang, L., Dong, H., Alelaiwi, A. and Saddik, A. El 2015. Evaluating and improving the depth accuracy of Kinect for Windows v2. 15, 8 (Aug. 2015), 4275--4285.Google Scholar
- Linder, T., Wehner, S. and Arras, K.O. 2015. Real-time full-body human gender recognition in (RGB)-D data. (Jun. 2015), 3039--3045.Google Scholar
- Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E. and Phillips, J.C. 2008. GPU computing. 96, 5 (May 2008), 879--899.Google Scholar
- Ye, Q. and Gui, P.P. 2015. A new calibration method for depth sensor. 26, 6 (Jun. 2015), 1146--1151.Google Scholar
- Zhang, S., He, W., Yu, Q. and Zheng, X. 2012. Low-cost interactive whiteboard using the Kinect. (2012), 38--42.Google Scholar
- Jafari, O.H., Mitzel, D. and Leibe, B. 2014. Real-time RGB-D based people detection and Tracking for mobile robots and head-worn cameras. (Sep. 2014), 5636--5643.Google Scholar
- Andaluz, V.H., Gallardo, C., Santana, J., Villacres, J., Toasa, R., Vargas, J., Reyes, G., Naranjo, T. and Sotelo, A. 2012. Bilateral virtual control human-machine with kinect sensor. (2012), 101--104. Google ScholarDigital Library
- Sell, J. and O'Connor, P. 2014. The xbox one system on a chip and kinect sensor. 34, 2 (2014), 44--53.Google Scholar
- Jha, S. and Trivedi, P. 2013. An automated video surveillance system using Viewpoint Feature Histogram and CUDA-enabled GPUs. (2013), 1812--1816.Google Scholar
- Chuan, C.H., Chen, Y.N. and Fan, K.C. 2016. Human action recognition based on action forests model using kinect camera. (May 2016), 914--917.Google Scholar
- Yao, H., Ge, C., Xue, J. and Zheng, N. 2017. A high spatial resolution depth sensing method based on binocular structured light. 17, 4 (Apr. 2017).Google Scholar
- Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S. and Fitzgibbon, A. 2011. KinectFusion: Real-time dense surface mapping and tracking. (2011), 127--136. Google ScholarDigital Library
- OpenCV library: http://opencv.org/.Google Scholar
- {OpenGL - The Industry Standard for High Performance Graphics: https://www.opengl.org/.Google Scholar
- Unity - Manual: DirectX 11 and OpenGL Core: https://docs.unity3d.com/Manual/UsingDX11GL3Features.html.Google Scholar
- Fernández-Cervantes, V., García, A., Ramos, M.A., Méndez, A. and Méndez, A. 2015. Facial Geometry Identification through Fuzzy Patterns with RGBD Sensor. Computación y Sistemas. 19, 3 (Oct. 2015), 529--546.Google Scholar
- Allusse, Y., Horain, P., Agarwal, A. and Saipriyadarshan, C. 2008. GpuCV: An opensource GPU-accelerated framework for image processing and computer vision. (2008), 1089--1092. Google ScholarDigital Library
- CUDA - OpenCV library: http://opencv.org/platforms/cuda.html.Google Scholar
- Procesamiento paralelo CUDA | Qué es CUDA | NVIDIA: http://www.nvidia.es/object/cuda-parallel-computing-es.html.Google Scholar
- Carraro, M., Munaro, M. and Menegatti, E. 2016. Cost-efficient RGB-D smart camera for people detection and tracking. 25, 4 (Jul. 2016).Google Scholar
- Munaro, M., Basso, F. and Menegatti, E. 2016. OpenPTrack: Open source multi-camera calibration and people tracking for RGB-D camera networks. 75, (Jan. 2016), 525--538. Google ScholarDigital Library
- Introduction --- OpenCV 2.4.13.3 documentation: http://docs.opencv.org/2.4/modules/core/doc/intro.html.Google Scholar
Index Terms
- Kinect sensor performance for Windows V2 through graphical processing
Recommendations
Depth completion for kinect v2 sensor
Kinect v2 adopts a time-of-flight (ToF) depth sensing mechanism, which causes different type of depth artifacts comparing to the original Kinect v1. The goal of this paper is to propose a depth completion method, which is designed especially for the ...
Depth analysis of kinect v2 sensor in different mediums
AbstractFrom the last few years, RGB-D cameras are widely used by researchers in various fields. Their reasonable cost and the ability to estimate distances at a high frame rate have made these sensors recommendable for applications in gaming accessories, ...
A metrological characterization of the Kinect V2 time-of-flight camera
A metrological characterization process for time-of-flight (TOF) cameras is proposed in this paper and applied to the Microsoft Kinect V2. Based on the Guide to the Expression of Uncertainty in Measurement (GUM), the uncertainty of a three-dimensional (...
Comments