Abstract
Our previous studies demonstrated that the idea of bio-monitoring home healthcare mobile robots is feasible. Therefore, by developing algorithms for mobile robot based tracking, measuring, and activity recognition of human subjects, we would be able to help impaired people (MIPs) to spend more time focusing in their motor function rehabilitation process from their homes.
In this study we aimed at improving two important modules in these kinds of systems: the control of the robot and visual tracking of the human subject. For this purpose: 1) tracking strategies for different types of home environments were tested in a simulator to investigate the effect on robot behavior; 2) a multi-channel saliency fusion model with high perceptual quality was proposed and integrated into RGB-D based visual tracking.
Regarding the control strategies, results showed that, depending on different types of room environment, different tracking strategies should be employed. For the visual tracking, the proposed saliency fusion model yielded good results by improving the saliency output. Also, the integration of this saliency model resulted in better performance of RGB-D based visual tracking application.
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References
Nergui, M., Imamoglu, N., Yoshida, Y., Yu, W.: Human gait behavior classification using hmm based on lower body triangular joint features. In: IASTED International Conference on Signal and Image Processing, Honolulu (2012)
Nergui, M., Imamoglu, N., Yoshida, Y., Gonzalez, J., Otake, M., Yu, W.: Human Activity Recognition Using Body Contour Parameters Extracted from Depth Images. J. of Medical Imaging & Health Informatics (accepted, 2013)
Smith, L.I.: A Tutorial on Principal Component Analysis, http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf
Wang, Z., Ma, Y.: Medical image fusion using m-PCNN. Information Fusion 9, 176–185 (2008)
Paris, S.: Particle Filter Color Tracker. In: Mathworks File Exchange (2011), http://www.mathworks.com/matlabcentral/fileexchange/17960-particle-filter-color-tracker
Giebel, J., Gavrila, D.M., Schnörr, C.: A Bayesian Framework for Multi-cue 3D Object Tracking. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 241–252. Springer, Heidelberg (2004)
Frintrop, S., Konigs, A., Hoeller, F., Schulz, D.: A Component Based Approach to Visual Person Tracking from a Mobile Platform. Int. J. of Social Robotics 2, 53–62 (2010)
Itti, L., Koch, C., Niebur, E.: Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Achanta, R., Hemami, S., Estrada, F., Susstrunk, S.: Frequency-Tuned Salient Region Detection. In: IEEE Int. Conf. CVPR, pp. 1597–1604 (2009)
Imamoglu, N., Lin, W., Fang, Y.: A Saliency Detection Model Using Low-Level Features Based on Wavelet Transform. IEEE Trans. on MultiMedia 15(1), 96–105 (2013)
Hou, X., Zhang, L.: Saliency Detection: A Spectral Residual Approach. In: IEEE Int. Conf. CVPR, pp. 1–8 (2007)
Liu, T., Sun, J., Zheng, N.-N., Tang, X., Shum, H.-Y.: Learning to Detect a Salient Object. In: IEEE Int. Conf. CVPR, pp. 1–8 (2007)
Cardillo, G.: ROC curve: compute a Receiver Operating Characteristics curve (2008), http://www.mathworks.com/matlabcentral/fileexchange/19950
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Imamoglu, N. et al. (2013). An Improved Saliency for RGB-D Visual Tracking and Control Strategies for a Bio-monitoring Mobile Robot. In: Botía, J.A., Álvarez-García, J.A., Fujinami, K., Barsocchi, P., Riedel, T. (eds) Evaluating AAL Systems Through Competitive Benchmarking. EvAAL 2013. Communications in Computer and Information Science, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41043-7_1
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DOI: https://doi.org/10.1007/978-3-642-41043-7_1
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