This thesis presents an effective multi-layer background modelingmethod to detect moving objects by exploiting the advantage of distinctive features and hierarchical structure of the Codebook (CB) model. In the process, two image features are involved, namely the mean RGB feature and the Binary Ordered Dithering (BOD) feature.The mean RGB feature is one of the most fundamental features employed in moving-object detection applications. However, in the block-based structure, the mean-color feature within a block often does not contain sufficient texture information, causing incorrect classification especially in large block size layers.Conversely, binary bitmap generated from the Ordered Dithering (OD) is a more effective candidate for the estimation of texture information within individual blocks. Thus, the BOD feature becomes an important supplement to the mean RGB feature for the formation of a novel discriminative feature in a block-based object detection system. The background model described in this thesis consists of four layers, which can be categorized into three block-based layers and onepixel-based layer. The block-based layers are employed for the efficient removal the background, and the pixel-based layer is for foreground refinement. To further improve the detection results, several additional steps are included, such as the shadow and highlight removal for the identification of the true foreground. Moreover, the Long-term Stationary Foreground Removal (LSFR) method is employed for the determination of the stationary foreground. And the Isolated False Positive Foreground Removal (IFPFR) technique is used for the removal of the isolated foreground pixel to improve the final detected result. In summary, the uniqueness of this approach is the incorporation of the halftoningscheme with the codebook model for superior performance over the existing methods

This thesis presents an effective multi-layer background modelingmethod to detect moving objects by exploiting the advantage of distinctive features and hierarchical structure of the Codebook (CB) model. In the process, two image features are involved, namely the mean RGB feature and the Binary Ordered Dithering (BOD) feature.The mean RGB feature is one of the most fundamental features employed in moving-object detection applications. However, in the block-based structure, the mean-color feature within a block often does not contain sufficient texture information, causing incorrect classification especially in large block size layers.Conversely, binary bitmap generated from the Ordered Dithering (OD) is a more effective candidate for the estimation of texture information within individual blocks. Thus, the BOD feature becomes an important supplement to the mean RGB feature for the formation of a novel discriminative feature in a block-based object detection system. The background model described in this thesis consists of four layers, which can be categorized into three block-based layers and onepixel-based layer. The block-based layers are employed for the efficient removal the background, and the pixel-based layer is for foreground refinement. To further improve the detection results, several additional steps are included, such as the shadow and highlight removal for the identification of the true foreground. Moreover, the Long-term Stationary Foreground Removal (LSFR) method is employed for the determination of the stationary foreground. And the Isolated False Positive Foreground Removal (IFPFR) technique is used for the removal of the isolated foreground pixel to improve the final detected result. In summary, the uniqueness of this approach is the incorporation of the halftoningscheme with the codebook model for superior performance over the existing methods

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