Abstract
Detecting humans and objects in images has been a very challenging problem due to variation in illumination, pose, clothing, background and other complexities. Depth information is an important cue when humans recognize objects and other humans. In this work we utilize the depth information that a Kinect sensor - Xtion Pro Live provides to detect humans and obstacles in real time for a blind or visually impaired user. The system runs in two modes. For the first mode, we focus on how to track and/or detect multiple humans and moving objects and transduce the information to the user. For the second mode, we present a novel approach on how to avoid obstacles for safe navigation for a blind or visually-impaired user in an indoor environment. In addition, we present a user study with some blind-folded users to measure the efficiency and robustness of our algorithms and approaches.
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References
Zöllner, M., Huber, S., Jetter, H.-C., Reiterer, H.: NAVI – A Proof-of-Concept of a Mobile Navigational Aid for Visually Impaired Based on the Microsoft Kinect. In: Campos, P., Graham, N., Jorge, J., Nunes, N., Palanque, P., Winckler, M. (eds.) INTERACT 2011, Part IV. LNCS, vol. 6949, pp. 584–587. Springer, Heidelberg (2011)
Bourbakis, N.: Sensing Surround 3-D Space for Navigation of the Blind. IEEE Engineering in Medicine and Biology Magazine 27(1) (January-February 2008)
Meers, S., Ward, K.: A Substitute Vision System for Providing 3D Perception and GPS Navigation via Electro-Tactile Stimulation. School of IT and Computer Science University of Wollongong, Wollongong, NSW, Australia (2005)
Cunha, J., et al.: Using a Depth Camera for Indoor Robot Localization and Navigation. DETI/IEETA- University of Aveiro, Portugal (2011)
Dakopoulos, D., Bourbakis, N.: Wearable Obstacle Avoidance Electronic Travel Aids for Blind: A Survey. IEEE Trans. on Systems, Man, and Cybernetics, January 1 (2010)
Xia, L., Chen, C.-C., Aggarwal, J.K.: Human Detection Using Depth Information by Kinect. In: International Workshop on Human Activity Understanding from 3D Data in Conjunction with CVPR (HAU3D), Colorado Springs, CO (June 2011)
Ran, L., Helal, S., Moore, S.: Drishti: An Integrated Indoor/Outdoor Blind Navigation System and Service. In: PerCom, pp. 23–32 (2004)
Cardin, S., Thalmann, D., Vexo, F.: Wearable Obstacle Detection System for visually impaired People. Visual Computer: International Journal of Computer Graphics archive 23(2) (January 2007)
OpenNI Program Guide, http://www.openni.org/Documentation/
OpenCV, http://www.opencv.itseez.com
MSDNS. SAPI Function. Microsoft, August 28 (2008), http://msdn.microsoft.com/en-us/library/aa911241.aspx
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© 2012 Springer-Verlag Berlin Heidelberg
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Khan, A., Moideen, F., Lopez, J., Khoo, W.L., Zhu, Z. (2012). KinDectect: Kinect Detecting Objects. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds) Computers Helping People with Special Needs. ICCHP 2012. Lecture Notes in Computer Science, vol 7383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31534-3_86
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DOI: https://doi.org/10.1007/978-3-642-31534-3_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31533-6
Online ISBN: 978-3-642-31534-3
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