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Motion Segmentation Based on Structure-Texture Decomposition and Improved Three Frame Differencing

Abstract : Motion segmentation from the video datasets has several important applications like traffic monitoring, action recognition, visual object tracking, and video surveillance. The proposed technique combines the structure-texture decomposition and the improved three frames differencing for motion segmentation. First, the Osher and Vese approach is employed to decompose the video frame into two components, viz., structure and texture/noise. Now, to eliminate the noise, only the structure components are employed for further steps. Subsequently, the difference between (i) the current frame and the previous frame as well as (ii) the current frame and the next frame are estimated. Next, both the difference frames are combined using pixel-wise maximum operation. Each combined difference frame is then partitioned into non-overlapping blocks, and the intensity sum as well as median of each block is computed. Successively, target objects are detected with the help of threshold and intensity median. Finally, post-processing in the form of morphology operation and connected component analysis is carried out to accurately find the foreground. Our technique has been formulated, implemented and tested on publicly available standard benchmark datasets and it is proved from performance analysis that our technique exhibit efficient outcomes than existing approaches.
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Submitted on : Thursday, October 24, 2019 - 12:50:05 PM
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Sandeep Sengar. Motion Segmentation Based on Structure-Texture Decomposition and Improved Three Frame Differencing. 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2019, Hersonissos, Greece. pp.609-622, ⟨10.1007/978-3-030-19823-7_51⟩. ⟨hal-02331299⟩

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