Dr. Xiaoran Guo Profile

Dr. Xiaoran Guo

at Ordnance Engineering College

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Proceedings Article | 24 November 2014 Paper

Improved mean shift target tracking approach under the interference of background

Xiaoran Guo, Shaohui Cui, Dan Fang

Proceedings Volume 9301, 930119 (2014) https://doi.org/10.1117/12.2071320

KEYWORDS: Detection and tracking algorithms, Bismuth, Image enhancement, Current controlled current source, Target recognition, RGB color model, Missiles, Statistical methods, Statistical analysis, Optoelectronics

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Aiming at the earlier stage tracking of strapdown image terminal guidance system, this paper propose a tracking approach which can not only enhance the distinction between targets and background effectively, but also can constrain the interference of target positioning suffered from background information. Among various target tracking approaches, the Mean Shift tracking algorithm is an excellent one due to its efficiency and simplicity, while it can not effective restrain the disturbance from background information. Thus, in this paper, an only target model background-weighted histogram target tracking algorithm, which can restrain the interference from background information, is presented under the Mean Shift framework. If the histogram of target model and target candidate model are both transformed, the probability of remarkable background features in the target model and target candidate model will simultaneously decrease. Thus it is equivalent to a proportional transformation of the weights obtained by the conventional target representation method. Meanwhile, the Mean Shift iteration formula is invariant to the proportional transformation of weights. Therefore, simultaneously transform the histogram of target model and target candidate model is exactly the same as the Mean Shift tracking with traditional target representation. So the proposed algorithm only transforms the histogram of target model and decreases the probability of target model features that are prominent in the background, but do nothing to target candidate model. Experimental results show that the proposed algorithm can not only restrain the disturbance from background information and improve the tracking accuracy, but also not increases the execution time.

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