Abstract
Elderly and disabled people can particularly benefit from smart environments with integrated sensors, as they offer basic assistive functionalities enabling personal independence and increased safety. In a smart environment, the key issue is to quickly sense the location and identity of its users. In this paper, we aim at enhancing the robustness of human detection and identification algorithm in a home environment based on the Kinect, which is a new and multimodal sensor. The contribution of our work is that we employ different cameras for different algorithmic modules, based on investigating the suitability of each camera in Kinect for a specific processing task, resulting in an efficient and robust human detection, tracking and re-identification system. The total system consists of three processing modules: (1) object labeling and human detection based on depth data, (2) human reentry identification based on both RGB and depth information, and (3) human tracking based on RGB data. Experimental results show that each algorithmic module works well, and the complete system can accurately track up to three persons in a real situation.
The major work was done while Jungong Han was working in CWI.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arif O, Daley W, Vela P, Teizer J, Stewart J (2010) Visual tracking and segmentation using time-of-flight sensor. In: Proceedings of ICIP, pp 2241–2244
Bak S, Corvee E, Bremond F, Thonnat M (2010) Person re-identification using spatial covariance regions of human body parts. In: Proceedings of IEEE conference on advanced video and signal-based surveillance, pp 435–440
Bevilacqua A, Di L, Azzari S (2006) People tracking using a time-of-flight depth sensor. In: Proceedings of IEEE international conference video and signal based surveillance, pp 89–93
Crabb R, Tracey C, Puranik A, Davis J (2008) Real-time foreground segmentation via range and color imaging. In: Proceedings of CVPR workshop on TOF-CV
Felzenszwalb P, Huttenlocher D (2004) Efficient graph-based image segmentation. Int J Comp Vis 59(2):167–181
Gould S, Baumstarck P, Quigley P, Ng M, Koller A (2008) Integrating visual and range data for robotic object detection. In: Proceedings of ECCV workshop on multi-camera and multimodal sensor fusion
Hamdoun O, Moutarde F, Stanciulescu B, Steux B (2008) Person re-identification in multicamera system by signature based on interest point descriptors collected on short video sequences. In: Proceedings of international conference on distributed smart cameras, pp 16
Han J, Pauwels E, de Zeeuw P, de With P (2012) Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment. IEEE Trans Consum Electron 58(2):255–263
Hansen D, Hansen M, Kirschmeyer M, Larsen R, Silvestre D (2008) Cluster tracking with time-of-flight cameras. In: Proceedings of CVPR workshop on TOF-CV
Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with Microsoft Kinect sensor: a review. IEEE T Cybernetics 43(5):1318–1334
Hirzer M, Roth M, Kstinger M, Bischof H (2012) Relaxed pairwise learned metric for person re-identification. In: Proceedings of ECCV, Springer, pp 780793.
Nakajima C, Pontil M, Heisele B, Poggio T (2003) Full-body person recognition system. Pattern Recogn 36(9):1997–2006
Rodriguez M, Shah M (2007) Detecting and segmenting humans in crowded scenes. In: Proceedings of ACM multimedia, pp 353–356
Sabeti L, Parvizi E, Wu Q (2008) Visual tracking using color cameras and time-of-flight range imaging sensors. J Multimedia 3(2):28–36. (ACM)
Salas J, Tomasi C (2011) People detection using color and depth images. Pattern Recogn, Springer 6718:127–135
Sklansky J (1982) Finding the convex hull of a simple polygon. PRL 1:79–83
Spinello L, Arras KO (2011) People detection in RGB-D data. In: Proceedings of IROS
Suzuki S, Abe K (1985) Topological structural analysis of digitized binary images by border following. CVGIP 30(1):32–46
Xia L, Chen C, Aggarwal JK (2011) Human detection using depth information by Kinect. In: Proceedings of computer vision and pattern recognition workshops (CVPRW), pp 15–22
Acknowledgments
The major work has been done while Jungong Han was employed by CWI, the Netherlands. Therefore, we would like to thank Dr. Eric Pauwels for granting the opportunity to work on this interesting topic.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Han, J., Han, J. (2014). RGB-D Human Identification and Tracking in a Smart Environment. In: Shao, L., Han, J., Kohli, P., Zhang, Z. (eds) Computer Vision and Machine Learning with RGB-D Sensors. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-08651-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-08651-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08650-7
Online ISBN: 978-3-319-08651-4
eBook Packages: Computer ScienceComputer Science (R0)