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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 3/2016

01.07.2016

Bounding Multiple Gaussians Uncertainty with Application to Object Tracking

verfasst von: Baochang Zhang, Alessandro Perina, Zhigang Li, Vittorio Murino, Jianzhuang Liu, Rongrong Ji

Erschienen in: International Journal of Computer Vision | Ausgabe 3/2016

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Abstract

This paper proves the uncertainty bound for the multiple Gaussian functions, termed multiple Gaussians Uncertainty (MGU), which significantly generalizes the uncertainty principle for the single Gaussian function. First, as a theoretical contribution, we prove that the momentum (velocity) and position for the sum of multiple Gaussians wave function are theoretically bounded. Second, as for a practical application, we show that the bound can be well exploited for object tracking to detect anomalies of local movement in an online learning framework. By integrating MGU with a given object tracker, we demonstrate that uncertainty principle can provide remarkable robustness in tracking. Extensive experiments are done to show that the proposed MGU can significantly help base trackers overcome the object drifting and reach state-of-the-art results.
Vorheriger Artikel Similarity Fusion for Visual Tracking
Nächster Artikel Spectral Generalized Multi-dimensional Scaling

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Fußnoten
1
Similar considerations hold in quantum theory that momentum(mass and velocity) and position cannot be exactly measured simultaneously
 
2
Gaussian pdfs are particular cases.
 
3
We will consider the non-zero mean situation in the additional material, see Theorem 2.
 
4
Note that the Multiple Gaussian Uncertainty is still valid when \(\mu \) is non-zero, and the proof is given in Theorem 2 in the additional material
 
5
It is obtained by substituting \({\mathcal {U}}_H(.)\) with \({\mathcal {D}}(.)\)
 
Literatur
Adam, A., Rivlin, E., & Shimshoni, I. (2006). Robust fragments-based tracking using the integral histogram. CVPR.
Ali, S., & Shah, M. (2008). Floor fields for tracking in high density crowd scenes. In Proceedings of ECCV.
Avidan, S. (2004). Support vector tracking. IEEE Transactions on PAMI, 26(8), 1064–1072.CrossRef
Babenko, B., Yang, M., & Belongie, S. (2011). Robust object tracking with online multiple instance learning. IEEE Trans. PAMI, 6.
Betke, M., Hirsh, D., Bagchi, A., Hristov, N., Makris, N., & Kunz, T. (2007). Tracking large variable numbers of objects in clutter. In Proceedings of IEEE CVPR (pp. 1–8).
Born & Jordan. (1926). In the relation between the quantum mechanics of Heisenberg. Analen der Physik(4), 79.
Brostow, G., & Cipolla, R. (2006). Unsupervised bayesian detection of independent motion in crowds. In Proceedings of IEEE CVPR, (Vol. 1, pp. 594–601).
Canny, J. (2009). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Cehovin, L., Kristan, M., & Leonardis, A. (2013). Robust visual tracking using an adaptive coupled-layer visual model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4), 941–953.CrossRef
Chu, C.-T., Hwang, J.-N., Pai, H.-I., & Lan, K.-M. (2013). Tracking human under occlusion based on adaptive multiple kernels with projected gradients. IEEE Transactions on Multimedia, 15(7), 1602–1615.CrossRef
de Broglie, L. (1929). The wave nature of the electron. Nobel Lecture, 12.
Doulamis, A. D. (2009). Adaptable neural networks for objects’ tracking re-initialization. ICANN, 2, 715–724.
Duffner, S., & Garcia, C. (2013). PixelTrack: A fast adaptive algorithm for tracking non-rigid objects. In Proceedings of ICCV.
Felzenszwalb, P. F., Girshick, R. B., McAllester, D. A., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on PAMI, 32(9), 1627–1645.CrossRef
Gabor, D. (1946). Theory of communication. The Journal of the Institute of Electrical Engineers, 93(26), 429C457. pt. III.
Gilbert, A., & Bowden, R. (2007). Multi person tracking within crowded scenes. In IEEE workshop on human motion (pp. 166–179).
Godec, M., Roth, P. M., & Bischof, H. (2013). Hough-based tracking of non-rigid objects. Computer Vision and Image Understanding, 117(10), 1245–1256.CrossRef
Grabner, H., Grabner, M., & Bischof, H. (2008). Semi-supervised on-line boosting for robust tracking. In Proceedings of ECCV (pp. 234–247).
Grabner, H., Grabner, M., & Bischof, H. (2006). Real-time tracking via on-line boosting. Proceedings of BMVC, 1, 47–56.
Gustafson, F., Gunnison, F., & Nicolas, B. (2002). Particle filters for position, navigation, and tracking. IEEE Transactions on Signal Processing, 50(2), 425–437.CrossRef
Han, Z., Jiao, J., Zhang, B., Ye, Q., & Liu, J. (2011). Visual object tracking via sample-based adaptive sparse representation. Pattern Recognition Journal.
Hardy, G. H., Littlewood, J. E., & Pólya. (1967). Inequalities. Cambridge Mathematical Library.
Hare, S., Saffari, A., & Torr, P. (2011). Struck: Structured output tracking with kernels. In Proceedings of IEEE ICCV (pp. 263-270).
Hare, S., Saffari, A., & Torr,P. H. S. (2012). Efficient online structured output learning for keypoint-based object tracking. In Proceedings of CVPR (p. 1894)
Hariharakrishnan, K., & Schonfeld, D. (2005). Fast object tracking using adaptive block matching. IEEE Transactions on Multimedia, 853–859.
Hodge, V.J., & Austin, J. (2004). A survey of outlier detection methodologies. Artificial Intelligence Review.
Hu, W., Li, X., Zhang, X., Shi, X., Maybank, S., & Zhang, Z. (2011). Incremental tensor subspace learning and its applications to foreground segmentation and tracking. International Journal of Computer Vision (IJCV), 91(3), 303–327.CrossRefMATH
Jacobs, N., Dixon, M., & Pless, R. (2008). Location-specific transition distributions for tracking. In IEEE workshop on motion and video computing.
Jepson, A., Fleet, D., & El-Maraghi, T. ( 2003). Robust online appearance models for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1296–1311.
Kalal, Z., Mikolajczyk, K., & Matas, J. (2010). Forward-backward error: automatic detection of tracking failures. In International conference on pattern recognition (pp. 23–26).
Kalal, Z., Mikolajczyk, K., & Matas, J. (2010). Tracking-learning-detection. IEEE Transactions on PAMI, 6.
Knutsson, H. (1994). Signal processing for computer vision. Springer: New York.
Kristan et al. (2014). The visual object tracking VOT2014 challenge results. In ECCV visual object tracking challenge workshop.
Kwon, J., & Lee, K. M. (2010). Visual tracking decomposition. In Proceedings of IEEE CVPR (pp. 1269–1276).
Kwon, J., & Lee, K. M. (2013). Minimum uncertainty gap for robust visual tracking. In Proceedings of IEEE CVPR.
Kwon, J., & Lee, K. Mu. (2014). Interval Tracker: Tracking by interval analysis. In Proceedings of IEEE CVPR.
Li, S. (1999). Discrete multi-gabor expansions. IEEE Tranactions on Information theory, 45.
Nestares, O., & Fleet, D. J. (2001). Probabilistic tracking of motion boundaries with spatiotemporal predictions. In Proceedings of IEEE CVPR (pp. 358–365).
Peng, C., et al. (2009). Contextual mixture tracking. IEEE Transactions on Multimedia, 11.2, 333–341.CrossRef
Purdy, T. P., Peterson, R. W., & Regal, C. A. (2013). Observation of radiation pressure shot noise on a macroscopic object. Science, 339(6121), 801–804.CrossRef
Qu, W., et al. (2007). Real-time distributed multiobject tracking using multiple interactive trackers and a magnetic-inertia potential model. IEEE Transactions on Multimedia, 9(3), 511–519.CrossRef
Ramakrishna, V., Sheikh, Y., & Kanade, T. (2013). Tracking human pose by tracking symmetric parts. In Proceedings of IEEE CVPR.
Rodriguez, M., Ali, S., & Kanade, T. (2009). Tracking in unstructured crowded scenes. In Proceedings of IEEE ICCV.
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008). Incremental learning for robust visual tracking. International Journal Computer Vision, 77, 125–141.CrossRef
Shinde, S., & Gadre, V. M. (2001). An uncertainty principle for real signals in the fractional Fourier transform domain. IEEE Transactions on Signal Processing, 49(11).
Stauder, J., & Ostermann, J. (1999). Detection of moving cast shadows for object segmentation. IEEE Transactions on Multimedia, 65–76.
Stauffer, C., & Grimson, W.E.L. (1999). Adaptive background mixture models for real-time tracking. In Proceedings of IEEE CVPR, 2, 246–252.
Wang, X., Hua, G., & Han, T. X. (2010). Discriminative tracking by metric learning. In Proceedings of ECCV (pp. 200–214).
Wilson, R., & Granlund, G. (1984). The uncertainty principle in image processing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6.
Wilson, R., & Goesta, H. (1984). Granlund the uncertainty principle in image processing. IEEE Transactions on PAMI, 6(6), 758–767.CrossRef
Wu, Yi., Jongwoo L., & Ming-Hsuan Y. (2013). Online object tracking: A benchmark. In Proc. of CVPR (pp. 2411–2418).
Wu, B., & Nevatia, R. (2006). Tracking of multiple, partially occluded humans based on static body part detection. In Proceedings of IEEE CVPR (pp. 951–958).
Yao, R., Shi, Q., Shen, C., Zhang, Y., & Hengel, A. V. (2013). Part-based visual tracking with online latent structural learning. In Proceedings of IEEE CVPR (pp. 25–27).
Zhang, B., & Li, Z. (2016). Alessandro Perina, Alessio Del Bue, Vittorio Murino, Jianzhuang Liu. IEEE Transactions on CSVT: Adaptive Local Movement Modeling (ALMM) for Object Tracking.
Zhang, K., Zhang, L., Yang, M., Zhang, D. (2014). Fast tracking via spatio-temporal context learning. ECCV.
Zhang, B., Gao, Y., Zhao, S., & Zhong, B. (2011). Kernel Similarity modeling of texture pattern flow for motion detection in complex background. IEEE Transactions on CSVT, 21(1), 29–38.
Zhang, K., Zhang, L., & Yang, M. H. (2014). Real-time compressive tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10), 2002–2015.CrossRef
Zhuang, B., Lu, H., Xiao, Z., & Wang, D. (2014). Visual tracking via discriminative sparse similarity map. IEEE Transactions on Image Processing, 23(4), 1872–1881.MathSciNetCrossRef
Metadaten
Titel
Bounding Multiple Gaussians Uncertainty with Application to Object Tracking
verfasst von
Baochang Zhang
Alessandro Perina
Zhigang Li
Vittorio Murino
Jianzhuang Liu
Rongrong Ji
Publikationsdatum
01.07.2016
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 3/2016
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-016-0880-y

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