Abstract
In this paper, we propose a mean-shift tracking method by using the novel back-projection calculation. The traditional back-projection calculation methods have two main drawbacks: either they are prone to be disturbed by the background when calculating the histogram of target-region, or they only consider the importance of a pixel relative to other pixels when calculating the back-projection of search-region. In order to solve the two drawbacks, we carefully consider the background appearance based on two priors, i.e., texture information of background, and appearance difference between foreground-target and background. Accordingly, our method consists of two basic steps. First, we present a foreground-target histogram approximation method to effectively reduce the disturbance from background. Moreover, the foreground-target histogram is used for back-projection calculation instead of the target-region histogram. Second, a novel back-projection calculation method is proposed by emphasizing the probability that a pixel belongs to the foreground-target. Experiments show that our method is suitable for various tracking scenes and is appealing with respect to robustness.
This research is sponsored by Natural Science Foundation of China(NSFC No. 60675012)
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References
Greiffenhagen, M., Comaniciu, D., Niemann, H., Ramesh, V.: Design, analysis and engineering of video monitoring systems: An approach and a case study. IEEE Proceedings 89, 1498–1517 (2001)
Bradski, G.R.: Computer vision face tracking as a component of a perceptual user interface. In: IEEE Workshop on Applications of Computer Vision, pp. 214–219 (1998)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 142–149 (2000)
Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Transaction on Pattern Analysis and Machine Intelligence 25, 564–577 (2003)
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition 21, 32–40 (1975)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
Lee, J.H., Lee, W.H., Jeong, D.S.: Object-tracking method using back-projection of multiple color histogram models 2, 668–671 (2003)
Robert Collins, Y.L., Leordeanu, M.: On-line selection of discriminative tracking features. IEEE Transaction on Pattern Analysis and Machine Intelligence 27, 1631–1643 (2005)
Lin, J.: Divergence measures based on the shannon entropy. IEEE Transactions on Information Theory 37, 145–151 (1991)
Collins, R., Zhou, X., Teh, S.K.: An open source tracking testbed and evaluation web site. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2005) (January 2005)
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Wang, L., Wu, H., Pan, C. (2010). Mean-Shift Object Tracking with a Novel Back-Projection Calculation Method. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_8
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DOI: https://doi.org/10.1007/978-3-642-12307-8_8
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