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

Almost in all computer vision applications, fast, enhanced and accurate foreground detection is a critical and challenging prerequisite. Ensuring the high level safety in crowded places is the key importance for the intelligent video surveillance system. Background modeling is the main essential for the robustness of the surveillance system and that could be achieved with the help of statistical model. The background can be approximate with the assist of numerous approaches but it should handle various datasets and real time video constraints. Background model does also have to assume the challenges like non stationary background. In this paper we have presents a novel and innovative approach for the indoor surveillance. Our objective is to sense the motion in indoor environment in occurrence of the clutter background, occlusions and specially, that could be in the low light. To achieve such an objective we have suggested a modified statistical approach for the background modeling. Backgrounds are approximate with the help of modeling and classify the foreground with the help of segmentation mask. Recursive mathematical filtering approach makes it easy to track the involved objects afterwards in the successive frames. The proposed indoor surveillance approach is entrenched with the universal tool MATLAB 2013. An experimental result exhibits the efficiency, robustness and the adaptability towards the environmental challenges.

Background Modeling, Computer Visio, Object Detection, Object Tracking

"Indoor Motion Detection using Modified GMM and Kalman", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.400-405, May 2019, Available :http://www.jetir.org/papers/JETIR1905Y58.pdf

"Indoor Motion Detection using Modified GMM and Kalman", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp400-405, May 2019, Available at : http://www.jetir.org/papers/JETIR1905Y58.pdf