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A Moving Target Recognition Algorithm Based on Improved Mixture Gaussian Background Model

Published: 27 December 2017 Publication History

Abstract

Many mutant factors in the video such as noise and illumination can easily lead to the moving target recognition error. The paper proposes a moving target recognition algorithm based on improved mixture Gaussian background model for ships and vehicles. The algorithm on account of mixture Gaussian background model and three-frame difference can obtain the potential target regions with less background under the condition of motion disturbance and light mutation in the background, extract the straight line, shape factor and Zernike moment features from the potential regions, and construct the least square support vector machine to identify the ships and vehicles. The experiment results show the algorithm can accurately identify the ships and vehicles.

References

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Xu Chun-xiu, Xu Gong-wen, Liao Ming-hai. 2015. The research of image segmentation technique in cross-media information retrieval. Network Security Technology & Application. 294, 3 (Jan. 2015), 58--59.
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Li Jun-bao, Yang Wen-hui, Xu Jian-qing. 2017. Deep Convolutional Network Based SAR Image Object Detection and Recognition. Navigation Position & Timing. 1, 23 (Dec. 2017), 102--110.
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Xu Bing, Niu Yanxiong, Lu Jianming. 2016. Object detection and segmentation algorithm in complex dynamic scene. Journal of Beijing University of Aeronautics and Astronautics. 42, 2 (Feb. 2016), 310--317.
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Sheng Jia-chuan, Yang Wei. 2015. Research on Moving Objects Detection in Video Sequences Based on Grabcut-guassian Mixture Model. Computer Engineering and Applications. 42, 5 (Nov. 2015), 198-201.
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F Perronnin, T Mensink, J Verbeek i. 2013. Image Classification with the Fisher Vector: Theory and Practice. International Journal of Computer Vision. 105, 6 (Mar. 2013), 222--245.

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  1. A Moving Target Recognition Algorithm Based on Improved Mixture Gaussian Background Model

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    ICVIP '17: Proceedings of the International Conference on Video and Image Processing
    December 2017
    272 pages
    ISBN:9781450353830
    DOI:10.1145/3177404
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Nanyang Technological University

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 27 December 2017

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    Author Tags

    1. mixture Gaussian background model
    2. moving target recognition
    3. multi-features
    4. potential region
    5. target detection

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