• Kritika Singh
• Vishal Gupta

Background Subtraction is a technique that detects foreground from images or videos streams. In Background Subtraction, the foreground elements are categorized after comparing the current frame with the background reference frame. Therefore, the background reference frame should be updated constantly so that it becomes adaptable to the changes in the background of the image or a video. This paper discusses and compares two such Background Subtraction techniques: Mixture of Gaussians (MOG) and K-Nearest Neighbor (KNN). The experimental analysis is done based on some important properties of both the approaches.

Background Subtraction, Mixture of Gaussian (MOG), K-Nearest Neighbor (KNN).

"A Comparative Study of MOG and KNN for Foreground Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.855-862, May-2018, Available :http://www.jetir.org/papers/JETIR1805563.pdf

"A Comparative Study of MOG and KNN for Foreground Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp855-862, May-2018, Available at : http://www.jetir.org/papers/JETIR1805563.pdf