Particle Filter Target Tracking Algorithm Based on the SIFT and Color Features Fusion

Ming Hua Liu; Chuan Sheng Wang; Xian Lun Wang

doi:10.4028/www.scientific.net/AMM.734.476

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 734Particle Filter Target Tracking Algorithm Based on...

Particle Filter Target Tracking Algorithm Based on the SIFT and Color Features Fusion

Abstract:

Aiming at the poor robustness problem of using single feature in the target tracking process, a novel tracking algorithm based on color and SIFT features fusion in particle filter framework is presented in complex environments. Color and SIFT features are selected to establish the target model according to their stability, The scale and rotation invariance of SIFT feature and resistance occlusion property of color feature has been fused in the particle filter framework adaptively. According to the dynamic change of the tracking scene, the fusion weights is updated adaptively. Experimental results show the proposed method can track target robustly under complex scene in real-time performance.

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Periodical:

Applied Mechanics and Materials (Volume 734)

Pages:

476-481

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.734.476

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Online since:

February 2015

Authors:

Ming Hua Liu*, Chuan Sheng Wang, Xian Lun Wang

Keywords:

Multi-Cues Fusion, Particle Filter, SIFT, Target Tracking

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* - Corresponding Author

References

[1] M. Isard, A. Blake: Int. J. of Computer Vision, Vol. 29(1) (1998), pp.5-28.

Google Scholar

[2] Comaniciu D, Ramesh V, Meer P: IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), Vol. 25(5) (2003), pp.564-577.

DOI: 10.1109/tpami.2003.1195991

Google Scholar

[3] Z. Kalal, J. Matas, K. Mikolajczyk: P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints, IEEE Conference on CVPR, 2010, pp.49-56.

DOI: 10.1109/cvpr.2010.5540231

Google Scholar

[4] O. Zoidi, N. Nikolaidis, A. Tefas, I. Pitas: Signal Processing: Image Communication, Vol. 29 (2014), pp.573-589.

DOI: 10.1016/j.image.2014.03.004

Google Scholar

[5] Zhu G P, Zeng Q S, Wang C H: Pattern Recognition, Vol. 39(11) (2006), pp.2223-2226.

Google Scholar

[6] X. Tan, B. Triggs: IEEE Transactions on Image Processing, Vol. 19(6) (2010), pp.1635-1650.

Google Scholar

[7] M. Roh, T. Kim, J. Park, S. Lee: Pattern Recognition Vol. 40 (2007), pp.931-943.

Google Scholar

[8] Z. Liu, H. Shen, G. Feng, D. Hu: Neurocomputing, Vol. 83 (2012), p.47–55.

Google Scholar

[9] Wang J Q, Yagi Y: IEEE Transactions on Image Processing, Vol. 17(2) (2008), pp.235-240.

Google Scholar

[10] Fazli S, Pour HM, Bouzari H: Particle Filter Based Object Tracking with Sift and Color Feature, 2nd Int. Conf. on Machine Vision, 2009, pp.89-93.

DOI: 10.1109/icmv.2009.47

Google Scholar

[11] Lowe D G: Int. J. of Computer Vision, Vol. 60(2) (2004), pp.91-110.

Google Scholar