Abstract
This article presents a novel approach for a real-time person tracking system based on particle filters that use different visual streams. Due to the difficulty of detecting a person from a top view, a new architecture is presented that integrates different vision streams by means of a Sigma-Pi network. A short-term memory mechanism enhances the tracking robustness. Experimental results show that robust real-time person tracking can be achieved.
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References
Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)
Bernardin, K., Gehrig, T., Stiefelhagen, R.: Multi-level particle filter fusion of features and cues for audio-visual person tracking. In: Stiefelhagen, R., Bowers, R., Fiscus, J.G. (eds.) RT 2007 and CLEAR 2007. LNCS, vol. 4625, pp. 70–81. Springer, Heidelberg (2008)
Hu, M.-K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8(2), 179–187 (1962)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artificial Intelligence 101(1-2), 99–134 (1998)
Nait-Charif, H., McKenna, S.J.: Activity summarisation and fall detection in a supportive home environment. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 4, pp. 323–326 (2004)
Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)
Sural, S., Qian, G., Pramanik, S.: Segmentation and histogram generation using the hsv color space for image retrieval. In: International Conference on Image Processing, vol. 2, pp. 589–592 (2002)
Swain, M.J., Ballard, D.H.: Color indexing. International Journal of Computer Vision 7, 11–32 (1991)
Thrun, S.: Particle filters in robotics. In: Proceedings of the 17th Annual Conference on Uncertainty in AI (UAI), vol. 1 (2002)
Triesch, J., Malsburg, C.: Democratic integration: Self-organized integration of adaptive cues. Neural Computation 13(9), 2049–2074 (2001)
West, G., Newman, C., Greenhill, S.: Using a Camera to Implement Virtual Sensors in a Smart House. In: From Smart Homes to Smart Care, pp. 83–90 (2005)
Zhang, H.B., Muhlenbein: Synthesis of Sigma-Pi neural networks by the breeder genetic programming. In: Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 1, pp. 318–323 (June 1994)
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Yan, W., Weber, C., Wermter, S. (2011). Person Tracking Based on a Hybrid Neural Probabilistic Model. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_47
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DOI: https://doi.org/10.1007/978-3-642-21738-8_47
Publisher Name: Springer, Berlin, Heidelberg
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