People Innovation Excellence
 

An Improved Version of Texture-based Foreground Segmentation: Block-based Adaptive Segmenter

Kahlil Muchtar, Faris Rahman, Tjeng Wawan Cenggoro, Arif Budiarto, Bens Pardamean

International Conference on Computer Science and Computational Intelligence 2018

Abstract: Foreground segmentation is one of moving object detection techniques of computer vision applications. To date, modern moving object detection methods require complex background modeling and thresholds tuning to confront illumination changes. This paper proposes an adaptive approach based on non-overlapping block texture representation. It aims to design a computationally light and efficient solution to improve the robustness of detection. We evaluate our proposed method on internal and public sequences and provide the quantitative and qualitative measurements. Experimental results show that the proposed method can improve the results of previous method and suitable for real-time challenges.

ResearchGate Link


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