Abstract
This paper presents an approach to real-time 3D object tracking in cluttered scenes using multiple synchronized and calibrated cameras. The goal is to accurately track targets over a long period of time in the presence of complete occlusion in some of the camera views. In the proposed system, color histogram was used to represent object appearance. Tracked 3D object locations were smoothed and new locations predicted using a Kalman filter. The predicted object 3D location was then projected onto all camera views to provide a search region for robust 2D object tracking and occlusion detection. The experimental results were validated using ground-truth data obtained from a marker-based motion capture system. The results illustrate that the proposed approach is capable of effective and robust 3D tracking of multiple objects in cluttered scenes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kang, J., Cohen, I., Medioni, G.: Tracking people in crowded scenes across multiple cameras. In: Asian Conference on Computer Vision (2004)
Mittal, A., Davis, L.S.: M2tracker: A multi-view approach to segmenting and tracking people in a cluttered scene using region-based stereo. International Journal of Computer Vision (IJCV) 3, 189–203 (2003)
Javed, O., Rasheed, Z., Alatas, O., Shah, M.: Knight: A real time surveillance system for multiple overlapping and non-overlapping cameras. In: 4th International Conference on Multimedia and Expo (2003)
Yue, Z., Zhou, S., Chellappa, R.: Robust two-camera tracking using homography. In: Proc. of IEEE Intl. Conf. on Acoustics, Speech and Signal Processing, pp. 1–4 (2004)
Black, J., Ellis, T., Rosin, P.: Multi view image surveillance and tracking. In: IEEE Workshop on Motion and Video Computing (2002)
Berclaz, J., Fleuret, F., Fua, P.: Robust people tracking with global trajectory optimization. In: IEEE Conf. on Computer Vision and Pattern Recognition (2006)
Nummiaro, K., Koller-Meier, E., Svoboda, T., Roth, D., Van Gool, L.: Color-based object tracking in multi-camera environments. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 591–599. Springer, Heidelberg (2003)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)
Zhu, S., Ma, K.K.: A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans. on Image Processing 9, 287–290 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, South Carolina, USA, vol. 2, pp. 142–149 (2000)
Svoboda, T., Martinec, D., Pajdla, T.: A convenient multi-camera self-calibration for virtual environments. PRESENCE: Teleoperators and Virtual Environments 14, 407–422 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jin, H., Qian, G., Rajko, S. (2006). Real-Time Multi-view 3D Object Tracking in Cluttered Scenes. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_65
Download citation
DOI: https://doi.org/10.1007/11919629_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48626-8
Online ISBN: 978-3-540-48627-5
eBook Packages: Computer ScienceComputer Science (R0)