References
- Trucco E. and Plakas K.. Video tracking: a concise survey. IEEE J. Ocean. Eng., 2006, 31, 520–529.
- Cheng H. Y. and Hwang J. N.. Adaptive particle sampling and adaptive appearance for multiple video object tracking. Signal Process., 2009, 89, 1844–1849.
- Pantrigo J. J., Hernandez J. and Sanchez A.. Multiple and variable target visual tracking for video-surveillance applications. Pattern Recognit. Lett., 2010, 31, 1577–1590.
- Li X., Wang K., Wang W. and Li Y.. A multiple object tracking method using Kalman filter, Proc. 2010 IEEE Int. Conf. on Information and automation: ICIA ’10, Harbin, China, June 2010, IEEE, 1862–1866.
- Weng S. K., Kuo C. M. and Tu S. K.. Video object tracking using adaptive Kalman filter. J. Vis. Commun. Image Represent., 2006, 17, 1190–1208.
- Li P., Zhang T. and Ma B.. Unscented Kalman filter for visual curve tracking. Image Vis. Comput., 2004, 22, 157–164.
- Hotta K.. Adaptive weighting of local classifiers by particle filters for robust tracking. Pattern Recognit., 2009, 42, 619–628.
- Wang Z., Yang X., Xu Y. and Yu S.. CamShift guided particle filter for visual tracking. Pattern Recognit. Lett., 2009, 30, 407–413.
- Pantrigo J. J., Sanchez A., Montemayor A. S. and Duarte A.. Multi-dimensional visual tracking using scatter search particle filter. Pattern Recognit. Lett., 2008, 29, 1160–1174.
- Shan C., Tan T. and Wei Y.. Real-time hand tracking using a mean shift embedded particle filter. Pattern Recognit., 2007, 40, 1958–1970.
- Li J. and Chua C. S.. Transductive local exploration particle filter for object tracking. Image Vis. Comput., 2007, 25, 544–552.
- Salmond D.. Target tracking: introduction and Kalman tracking filters, Proc. IEE Conf. on Target tracking: algorithms and applications, Farnborough, UK, October 2001, QinetiQ, Vol. 2, 1–16.
- Arulampalam M. S., Maskell S., Gordon N. and Clapp T.. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process., 2002, 50, 174–188.
- Isard M. and Blake A.. CONDENSATION — conditional density propagation for visual tracking. Int. J. Computer Vis., 1998, 29, 5–28.
- Djuric P. M., Kotecha J. H., Zhang J., Huang Y., Ghirmai T., Bugallo M. F. and Miguez J.. Particle filtering. IEEE Signal Proc. Mag., 2003, 20, 19–38.
- Chang W. Y., Chen C. S. and Jian Y. D.. Visual tracking in high-dimensional state space by appearance-guided particle filtering. IEEE Trans. Image Process., 2008, 17, 1154–1167.
- Lozano O. M. and Otsuka K.. Real-time visual tracker by stream processing. J. Signal Process. Syst., 2009, 57, 285–295.
- Shan Y., Sawhney H. S. and Kumar R. T.. Unsupervised learning of discriminative edge measures for vehicle matching between nonoverlapping cameras. IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, 700–711.
- Wang H. and Oliensis J.. Rigid shape matching by segmentation averaging. IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, 619–635.
- Karabiber F., Arena P., Fortuna L., Fiore S. D., Vagliasindi G. and Arik S.. Implementation of a moving target tracking algorithm using eye-RIS vision system on a mobile robot. J. Signal Process. Syst. [online] 2010. Available at: <http://www.springerlink.com/content/ck10797w66503627/>
- Kristensen F., Hedberg H., Jiang H., Nilsson P. and Owall V.. An embedded real-time surveillance system: implementation and evaluation. J. Signal Process. Syst., 2008, 52, 75–94.
- Bradski G. and Kaehler A. Learning OpenCV, 2008, 1st edition (O’Reilly Media, Sebastopol, CA).
- Comaniciu D., Ramesh V. and Meer P.. Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, 564–577.
- Comaniciu D. and Meer P.. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, 603–619.
- Cheng Y.. Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell., 1995, 17, 790–799.