• Dr. Navneet S. Ghedia
• Dr. C. H. Vithalani
• Dr. Ashish Kothari

Visual Monitoring System, background examination and modeling, segmentation and foreground detection under tough environments are the most considerable task. In this paper, we propose a novel performance approach for the GMM based indoor motion segmentation approach under different challenges. Generally, In a Computer vision or visual monitoring systems, a scene frequently requires the detection of non stationary objects as a prime requisite. Generally, researcher are focused on region based, pixel based or block based approach for the non stationary object detection, but amongst all the approaches the pixels based approach provides quicker result at the expenditure of sensitivity. It also depends on the object appearance and the shape. A enormous portion of the surveillance room depends on the image awareness, interpretation and recognition. We propose an competent and adaptive robust approach to detect the moving object in indoor environments. We have experienced our proposed approach with the various standard dataset and tested results with the accessible ground truth. The experimental results illustrate the robustness and flexibility to take the challenges for the indoor environments.

Background modeling, GMM, Thresholding, foreground Detection.

"Foreground Detection for Indoor Visual Monitoring System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.384-388, May-2019, Available :http://www.jetir.org/papers/JETIR1905Y55.pdf

"Foreground Detection for Indoor Visual Monitoring System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp384-388, May-2019, Available at : http://www.jetir.org/papers/JETIR1905Y55.pdf