Abstract
Domestic assistance for the elderly and impaired people is one of the biggest upcoming challenges of our society. Consequently, in-home care through domestic service robots is identified as one of the most important application area of robotics research. Assistive tasks may range from visitor reception at the door to catering for owner’s small daily necessities within a house. Since most of these tasks require the robot to interact directly with humans, a predominant robot functionality is to detect and track humans in real time: either the owner of the robot or visitors at home or both. In this article we present a robust method for such a functionality that combines depth-based segmentation and visual detection. The robustness of our method lies in its capability to not only identify partially occluded humans (e.g., with only torso visible) but also to do so in varying lighting conditions. We thoroughly validate our method through extensive experiments on real robot datasets and comparisons with the ground truth. The datasets were collected on a home-like environment set up within the context of RoboCup@Home and RoCKIn@Home competitions.
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References
Human figures average measurements. http://www.fas.harvard.edu/~loebinfo/loebinfo/Proportions/humanfigure.html
Rockin@home 2014 rulebook. http://rockinrobotchallenge.eu/rockin_home_rulebook.pdf
Ahmad, A., Xavier, J., Santos-Victor, J., Lima, P.: 3D to 2D bijection for spherical objects under equidistant fisheye projection. Computer Vision and Image Understanding 125, 172–183 (2014)
Camplani, M., Salgado, L.: Background foreground segmentation with RGB-D kinect data: An efficient combination of classifiers. Journal of Visual Communication and Image Representation 25(1), 122–136 (2014)
Cruz, L., Lucio, D., Velho, L.: Kinect and rgbd images: Challenges and applications. In: 25th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pp. 36–49. IEEE (2012)
Cucchiara, R., Prati, A., Vezzani, R.: A multi-camera vision system for fall detection and alarm generation. Expert Systems 24(5), 334–345 (2007)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Horprasert, T., Harwood, D., Davis, L.: A robust background subtraction and shadow detection. In: Proc. ACCV, pp. 983–988 (2000)
Kaushik, A., Celler, B.: Characterization of pir detector for monitoring occupancy patterns and functional health status of elderly people living alone at home. Technology and Health Care 15(4), 273–288 (2007)
Kulyukin, V., Gharpure, C., Nicholson, J., Pavithran, S.: RFID in robot-assisted indoor navigation for the visually impaired. In: 2004 IEEE/RSJ International Conference on Proceedings of the Intelligent Robots and Systems, (IROS 2004), vol. 2, pp. 1979–1984, September 2004
Noury, N., Herve, T., Rialle, V., Virone, G., Mercier, E., Morey, G., Moro, A., Porcheron, T.: Monitoring behavior in home using a smart fall sensor and position sensors. In: 1st Annual International, Conference on Microtechnologies in Medicine and Biology, pp. 607–610 (2000)
Otsu, N.: A threshold selection method from gray-level histograms. Automatica 11(285–296), 23–27 (1975)
Satake, J., Miura, J.: Robust stereo-based person detection and tracking for a person following robot. In: ICRA Workshop on People Detection and Tracking (2009)
Scheutz, M., McRaven, J., Cserey, G.: Fast, reliable, adaptive, bimodal people tracking for indoor environments. In: 2004 IEEE/RSJ International Conference on Proceedings of the Intelligent Robots and Systems, (IROS 2004), vol. 2, pp. 1347–1352, September 2004
Srichumroenrattana, N., Lursinsap, C., Lipikorn, R.: 2D face image depth ordering using adaptive hillcrest-valley classification and Otsu. In: 2010 IEEE 10th International Conference on Signal Processing (ICSP), pp. 645–648, October 2010
Vezzani, R., Grana, C., Cucchiara, R.: Probabilistic people tracking with appearance models and occlusion classification: The ad-hoc system. Pattern Recognition Letters 32(6), 867–877 (2011)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-511 (2001)
Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Ninth IEEE International Conference on Proceedings of the Computer Vision, pp. 734–741. IEEE (2003)
Wen-Hau, L., Wu, C., Fu, L.: Inhabitants tracking system in a cluttered home environment via floor load sensors. IEEE Transactions on Automation Science and Engineering 5(1), 10–20 (2008)
Xia, L., Chen, C., Aggarwal, J.: Human detection using depth information by kinect. In: 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 15–22. IEEE (2011)
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Sanz, D., Ahmad, A., Lima, P. (2016). Onboard Robust Person Detection and Tracking for Domestic Service Robots. In: Reis, L., Moreira, A., Lima, P., Montano, L., Muñoz-Martinez, V. (eds) Robot 2015: Second Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-319-27149-1_42
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DOI: https://doi.org/10.1007/978-3-319-27149-1_42
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