Abstract
One of the key techniques of multi-camera tracking systems is cross-view object tracking. Feature Matching (FM) and Field of View (FOV) based methods are adopted in conventional solutions towards this problem. However, FM is not computationally efficient and the results heavily depend on the parameter settings of the cameras. Therefore, it is not effective in practical applications. In addition, approaches based on FOV suffer from the delay of the detection of newly appeared objects. The results are not reliable if only consistent labelling is utilized. In this paper, we propose a novel scheme for cross-view object tracking based on Projective Invariants (PI) and FM. The experimental results show that, our method improves the performance of normal PI-based tracking algorithms. Especially, it provides accurate tracking performance in the case of multiple objects appear closely in the same area.
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References
Turk, M., Pentland, A.: Face recognition using eigen faces. Computer Visionand Patern Recognition, 586–591 (1991)
Osuna, E., Girosi, F., Freund, R.: Training support vector machines: an application to face detection. In: Proc. of CVPR, pp. 55–60 (1997)
Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2000)
Murase, H., Nayar, S.K.: Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision 14(1), 5–24 (1995)
Schneiderman, H., Kanade, T.: Probabilistic modeling of local appearance and spatial relationships for object recognition. In: Proc. of CVPR, pp. 45–51 (1998)
Darrel, T., Gordon, G., Harville, M., Woodll, J.: Integrated person tracking using stereo, color, and pattern detection. In: IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, pp. 601–608 (1998)
Wei, G., Petrushin, V., Gershman, A.: Multiple-camera people localization in a cluttered Environment. In: Proceedings of the fifth International Workshop on Multimedia Data Mining, pp. 52–60 (2004)
Utsumi, A., Tetsutani, N.: Human tracking using multiple-camera-based head appearance modeling. In: Proc. Of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, May 17-19, pp. 657–662 (2004)
Khan, S., Shah, M.: Consistent labeling of tracked objects in multiple cameras with Overlapping fields of view. IEEE Trans. on PAMI 25(10), 1355–1360 (2003)
Velipasalar, S., Wolf, W.: Recovering Field of view by using projective in-Variants. In: International Conference on Image Processing (ICIP), pp. 3069–3072 (2004)
Velipasalar, S., Wolf, W.: Multiple object tracking and occlusion handling by information exchange between uncalibrated cameras. In: IEEE International Conference on Image Processing, pp. 418–421 (2005)
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© 2008 Springer-Verlag Berlin Heidelberg
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Ouyang, N., Lin, Lp., Liu, Z. (2008). Cross-View Object Tracking by Projective Invariants and Feature Matching. In: Huang, YM.R., et al. Advances in Multimedia Information Processing - PCM 2008. PCM 2008. Lecture Notes in Computer Science, vol 5353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89796-5_88
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DOI: https://doi.org/10.1007/978-3-540-89796-5_88
Publisher Name: Springer, Berlin, Heidelberg
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