Abstract
This paper presents a new method to track multiple persons reliably using a network of smart cameras. The task of tracking multiple persons is very challenging due to targets’ non-rigid nature, occlusions and environmental changes. Our proposed method estimates the positions of persons in each smart camera using a maximum likelihood estimation and all estimates are merged in a fusion center to generate the final estimates. The performance of our proposed method is evaluated on indoor video sequences in which persons are often occluded by other persons and/or furniture. The results show that our method performs well with the total average tracking error as low as 10.2 cm. We also compared performance of our system to a state-of-the-art tracking system and find that our method outperforms in terms of both total average tracking error and total number of object loss.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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References
Ali, I., Dailey, M.N.: Multiple human tracking in high-density crowds. Image and Vision Computing 30(12), 966–977 (2012)
Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. on Pattern Analysis and Machine Intelligence 33(9), 1806–1819 (2011)
Bo Bo, N., Gruenwedel, S., Van Hese, P., Niño Castañeda, J., Van Haerenborgh, D., Van Cauwelaert, D., Veelaert, P., Philips, W.: Phd forum: Illumination-robust foreground detection for multi-camera occupancy mapping. In: Proceedings of the Sixth International Conference on Distributed Smart Cameras, ICDSC (2012)
Bredereck, M., Jiang, X., Korner, M., Denzler, J.: Data association for multi-object tracking-by-detection in multi-camera networks. In: The 2012 Sixth International Conference on Distributed Smart Cameras, ICDSC (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893 (2005)
Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(9), 1627–1645 (2010)
Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. on Pattern Analysis and Machine Intelligence 30, 267–282 (2008)
Gruenwedel, S., Jelača, V., Niño-Castañeda, J., Hese, P.V., Cauwelaert, D.V., Veelaert, P., Philips, W.: Decentralized tracking of humans using a camera network. In: Proceedings of SPIE, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, vol. 8301. SPIE (2012)
Khan, S.M., Shah, M.: Tracking multiple occluding people by localizing on multiple scene planes. IEEE Trans. on Pattern Analysis and Machine Intelligence 31, 505–519 (2009)
Papadakis, N., Bugeau, A.: Tracking with occlusions via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(1), 144–157 (2011)
Yun, Y., Gu, I.H., Aghajan, H.: Maximum-likelihood object tracking from multi-view video by combining homography and epipolar constraints. In: The 2012 Sixth International Conference on Distributed Smart Cameras, ICDSC (2012)
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Bo Bo, N. et al. (2013). Robust Multi-camera People Tracking Using Maximum Likelihood Estimation. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_53
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DOI: https://doi.org/10.1007/978-3-319-02895-8_53
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