吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (6): 1976-1985.doi: 10.13229/j.cnki.jdxbgxb201706041

• Orginal Article • Previous Articles     Next Articles

Blurred object detection based on improved particle filter in coal mine underground surveilance

YANG Chao-yu1, 2, LI Ce1, LIANG Yin-cheng1, YANG Feng1   

  1. 1.School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China;
    2.School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China
  • Received:2016-10-13 Online:2017-11-20 Published:2017-11-20

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Abstract: Regarding that the videos collected by intelligent monitoring system in coal mine underground are blur, a novel algorithm for unclear video object detection based on improved particle filter is proposed. In the framework of standard particle filter, a nonlinear, non-Gaussian and multi system state space fusion model is constructed based on frame difference. First, particle sampling and probability density propagation are realized in the region of the key points obtained in the frame difference image. Then, the weighted posterior sample particles are used to represent the posterior probability density of the multi system state space fusion model. The mean of the samples is used to estimate and fuse the posteriori state of the system. Finally, the system state space model is output to fulfill the object detection. Experiments are carried out using the video data of the Sanjiaohe coal mine underground monitoring system. The performance of the proposed model is evaluated by comparison with extended Kalman filter, unscented Kalman filter and particle filter. The improved accuracy and effectiveness of the proposed method are demonstrated.

Key words: information processing technology, object detection, particle filter, multi-state space, critical region sampling

CLC Number: 

  • TD76
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