Noise adaptive Kalman filter for video point target tracking

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Noise adaptive Kalman filter for video point target tracking
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                        Li LixingLi Lixing
1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China; 2. Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China; 3. University of Chinese Academy of Sciences, Beijing 100039, China
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Huang YongmeiHuang Yongmei
1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China; 2. Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
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Wang QiangWang Qiang
1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China; 2. Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
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He DongHe Dong
1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China; 2. Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China
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Affiliation:1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China; 2. Key Laboratory of Optical Engineering, Chinese Academy of Sciences, Chengdu 610209, China; 3. University of Chinese Academy of Sciences, Beijing 100039, China
Clc Number:TN29
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Abstract:

To overcome the difficulty of choosing the dynamic model and giving the strength of noise that applying the Kalman filter will face, this paper presents an algorithm of noise adaptive Kalman filtering for video point target tracking, based on the characteristic of video point targets. To better fit the dynamics of a video point target, the twostage dynamic model, which can changes its form with regard to the value of correlation time constant, has been chosen as the state transition model for Kalman filtering. Moreover, the process noise is estimated according to the dynamic model and the observation data. Meanwhile, the observation noise is estimated according to the grey value distribution in each image. Then the process noise and the observation noise are adaptive. According to the outfield experimental result, the methods we proposed could effectively ensure the tracking accuracy.

Key words:Kalman filtering; noise adaptive; point target

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Online: July 19,2017
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