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Multispectral images-based background subtraction using Codebook and deep learning approaches

Abstract : This dissertation aims to investigate the multispectral images in moving objects detection via background subtraction, both with classical and deep learning-based methods. As an efficient and representative classical algorithm for background subtraction, the traditional Codebook has first been extended to multispectral case. In order to make the algorithm reliable and robust, a self-adaptive mechanism to select optimal parameters has then been proposed. In this frame, new criteria in the matching process are employed and new techniques to build the background model are designed, including box-based Codebook, dynamic Codebook and fusion strategy. The last attempt is to investigate the potential benefit of using multispectral images via convolutional neural networks. Based on the impressive algorithm FgSegNet_v2, the major contributions of this part lie in two aspects: (1) extracting three channels out of seven in the FluxData FD-1665 multispectral dataset to match the number of input channels of the deep model, and (2) proposing a new convolutional encoder to utilize all the multispectral channels available to further explore the information of multispectral images.
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Submitted on : Monday, July 20, 2020 - 2:54:12 PM
Last modification on : Tuesday, July 21, 2020 - 4:22:48 AM


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  • HAL Id : tel-02902951, version 1


Rongrong Liu. Multispectral images-based background subtraction using Codebook and deep learning approaches. Image Processing [eess.IV]. Université Bourgogne Franche-Comté, 2020. English. ⟨NNT : 2020UBFCA013⟩. ⟨tel-02902951⟩



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