Background subtraction (BS) is the first step of various computer vision application specially those depending on motion tracking such as (car tacking, human recognition…etc.).Indeed, videos captured outdoors may contain a lot of undesirable changes
‘wind impact, illumination changes, weather conditions and others ’, generate numerous false positives.
This paper presents comparison between the simplest method for background extraction (background subtraction) and Gaussian Mixture Model which is common method in outdoors videos. These two method are then compared based on the ability of each one to detect moving object in outdoors videos especially with presence and absence of shadow in addition to other challenges like object movement in background, wind effect and camera instability. The results of this comparison is used to determine the suitable method for each state.
