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Proceedings Paper

Improved particle filtering algorithm based on the multi-feature fusion for small IR target tracking
Author(s): Er-you Ji; Guo-hua Gu; Wei-xian Qian; Lian-fa Bai; Xiu-bao Sui
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Paper Abstract

A Mean-shift Particle filtering tracking algorithm based on the multi-feature fusion has been raised in this paper. This algorithm mainly focus on the features of the high frequency histogram, fractal and the energy of the infrared small target, which directly against the defects exist in detecting the infrared small targets, such as the size of the target, the low tracking accuracy caused by the low SNR and so on. Since the particle filtering algorithm gives the advantage of multi-feature fusion, the algorithm raised in this paper combines the three features listed above and does the calculation using the particle weight to greatly improved the tracking accuracy. The clustering effect of the Mean-shift algorithm has also been applied to make the distribution of the particles more equals to the real target, which reduced the number of the particle and enhanced the real-time ability of the algorithm. The experimental results show that, this algorithm has better tracking accuracy, which gives more effectiveness in tracking the infrared small target compared to the traditional particle filtering algorithm.

Paper Details

Date Published: 8 September 2011
PDF: 9 pages
Proc. SPIE 8193, International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, 81931M (8 September 2011); doi: 10.1117/12.900152
Show Author Affiliations
Er-you Ji, Nanjing Univ. of Science and Technology (China)
Guo-hua Gu, Nanjing Univ. of Science and Technology (China)
Wei-xian Qian, Nanjing Univ. of Science and Technology (China)
Lian-fa Bai, Nanjing Univ. of Science and Technology (China)
Xiu-bao Sui, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8193:
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications
Jeffery J. Puschell; Junhao Chu; Haimei Gong; Jin Lu, Editor(s)

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