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An effective identification method of video ships and vehicles

Published: 16 August 2019 Publication History

Abstract

In usual videos, there are problems such as small amplitude motion interference and sudden change of illumination in the background. It is difficult for ship and vehicle recognition. This paper takes full advantage of classical mixture Gauss background model in the context of small-scale motion. The inter frame difference method is not sensitive to illumination changes. So this paper proposes an identification method combining mixture of Gauss background model and three-frame difference method, and achieves potential region acquisition. The connected components are analyzed and merged to obtain the minimum outer rectangles, and the geometric characteristics of the targets are extracted from the rectangles. This paper uses the LS_SVM classifier to classify each potential region and outputs whether there are targets and the location of the targets. The experiment results show the average accuracy of the target recognition method in this paper is significantly improved.

References

[1]
Li Weishuai, Jiang Yueqiu. Research on Ground Moving Target Recognition Technology Based on Multi-sensor. Optoelectronic Technology Application, 2016, 31(3):61--67.
[2]
Fan Xueman, Hu Shengliang. He Jingbo's Feature Extraction and Selection of Fully Polarized HRRP in Sea Radar Target Recognition. Journal of Electronics & Information Technology, 2016, 10(1):336--345.
[3]
Hu Yan, Wang Huiqin, Ma Zongfang. Adaptive smoke image segmentation algorithm based on improved Gaussian model. Journal of Computer-Aided Design & Computer Graphics, 2016, 7(2):230--233.
[4]
Shen Ting-ting, Zhong Si-dong, Yan Wen-hao. Improved Moving Target Detection Based on Edge Gaussian Mixture Model. Electro-optical & Control, 2016, 11(2): 231--235.

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  1. An effective identification method of video ships and vehicles

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    cover image ACM Other conferences
    AIPR '19: Proceedings of the 2nd International Conference on Artificial Intelligence and Pattern Recognition
    August 2019
    198 pages
    ISBN:9781450372299
    DOI:10.1145/3357254
    • Conference Chairs:
    • Li Ma,
    • Xu Huang
    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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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 August 2019

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    Author Tags

    1. LS_SVM classifier
    2. moving target
    3. potential region acquisition
    4. target recognition

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