Abstract
This paper presents a surveillance system for tracking multiple people through a wide area with sparsely distributed cameras. The computational core of the system is an adaptive probabilistic model for reasoning about peoples’ appearances, locations and identities. The system consists of two processing levels. At the low-level, individual persons are detected in the video frames and tracked at a single camera. At the high-level, a probabilistic framework is applied for estimation of identities and camera-to-camera trajectories of people. The system is validated in a real-world office environment with seven color cameras.
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References
Lerner, U., Parr, R.: Inference in hybrid networks: Theoretical limits and practical algorithms. In: Uncertainty in Artificial Intelligence, pp. 310–318 (2001)
Murphy, K.: Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California, Berkeley (2002)
Pasula, H., Russell, S., Ostland, M., Ritov, Y.: Tracking many objects with many sensors. In: Int. Joint Conf. on Artificial Intelligence, pp. 1160–1171 (1999)
Rasmussen, C.E.: The infinite Gaussian mixture model. In: Advances in Neural Information Processing Systems 12, pp. 554–560 (2000)
Zajdel, W.: Bayesian Visual Surveillance. From object detection to distributed cameras. PhD thesis, University of Amsterdam (2006)
Zajdel, W., Vlassis, N., Kröse, B.J.A.: Bayesian methods for tracking and localization. In: Aarts, E., Korts, J., Verhaegh, W. (eds.) Intelligent Algorithms, pp. 243–258. Kluwer Academic Publishers, Dordrecht (2005)
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Zajdel, W., Cemgil, A.T., Kröse, B.J.A. (2006). Dynamic Bayesian Networks for Visual Surveillance with Distributed Cameras. In: Havinga, P., Lijding, M., Meratnia, N., Wegdam, M. (eds) Smart Sensing and Context. EuroSSC 2006. Lecture Notes in Computer Science, vol 4272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11907503_22
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DOI: https://doi.org/10.1007/11907503_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47842-3
Online ISBN: 978-3-540-47845-4
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