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An improved tracking algorithm of floc based on compressed sensing and particle filter

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Abstract

In order to solve the problem of tracking flocs during complex flocculating process, we propose an improved algorithm combining particle filter (PF) with compressed sensing (CS). The feature of flocs image is extracted via CS theory, which is used to detect the single-frame image and get the detection value. Simultaneously, the optimal estimation of particle in the space model of non-linear and non-Gaussian state is obtained by PF. Then, we correlate the optimal estimate with the detected value to determine the trajectory of each particle and to achieve flock tracking. Experimental results demonstrate that this improved algorithm realizes the real-time tracking of flocs and calculation of sedimentation velocity. In addition, it eliminates the shortcomings of heavy computation and low efficiency in the process of extracting image features , and thus guarantees the accuracy and efficiency of tracking flocs.

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Acknowledgments

This work is supported by the National Natural Science Foundation, under Grant nos. 61640217, 41402290, 61462028, Science and Technology Support Program of Jiangxi Province, under Grant no. 20151BBE50055, and Landing Plan of Scientific and Technological Project of Jiangxi Provincial Colleges and Universities, under Grant no. KJLD2013037, Cultivation Plan of Leadership for Excellence Jiangxi Province and Poyang Lake 555 Engineering, under Grant no. S2013-57, and Science and Technology Project supported by education department of Jiangxi Province under Grant no. GJJ150541, and Nanchang City Sensor Network and Compressed Sensing Knowledge Innovation Team under Grant no. Hong Sci(2016)114.

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Authors and Affiliations

  1. School of Information Engineering, East China Jiaotong University, Nanchang, People’s Republic of China

    Xin Xie, Nan Jiang & Huandong Xiong

  2. Team of Intelligence Information, Xiangtan City Public Security Bureau, Hunan, People’s Republic of China

    Huiping Li

  3. School of Information Science Technology, East China Normal University, Shanghai, People’s Republic of China

    Mingye Xie

  4. School of Civil Engineering, East China Jiaotong University, Nanchang, People’s Republic of China

    Fengping Hu

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  1. Xin Xie
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  2. Huiping Li
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  3. Fengping Hu
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  4. Mingye Xie
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  5. Nan Jiang
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  6. Huandong Xiong
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Correspondence to Xin Xie.

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Xie, X., Li, H., Hu, F. et al. An improved tracking algorithm of floc based on compressed sensing and particle filter. Ann. Telecommun. 72, 631–637 (2017). https://doi.org/10.1007/s12243-017-0572-9

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  • DOI: https://doi.org/10.1007/s12243-017-0572-9

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