Abstract
This paper describes a system for tracking people and vehicles for stationary-camera visual surveillance. The appearance of objects being tracked is modeled using mixtures of mixtures of Gaussians. Particles filters are used to track the states of object. Results show the robustness of the system to various lighting and object conditions.
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Abd-Almageed, W., Davis, L.S.: Density Estimation using Mixture of Mixtures of Gaussians. In: 9th European Conference on Computer Vision (2006)
Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 24 (2002)
Han, H., Comaniciu, D., Zhu, Y., Davis, L.: Incremental Density Approximation and Kernel-Based Baesian Filtering for Object Tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2004)
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© 2007 Springer Berlin Heidelberg
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Abd-Almageed, W., Davis, L.S. (2007). Robust Appearance Modeling for Pedestrian and Vehicle Tracking. In: Stiefelhagen, R., Garofolo, J. (eds) Multimodal Technologies for Perception of Humans. CLEAR 2006. Lecture Notes in Computer Science, vol 4122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69568-4_17
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DOI: https://doi.org/10.1007/978-3-540-69568-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69567-7
Online ISBN: 978-3-540-69568-4
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