The first step in a surveillance system is to create a representation of the environment. Background subtraction is widely used algorithm to define a part of an image that most time remains stationary in a video. In surveillance tasks, this model helps to recognize those outlier objects in an area under monitoring. Set up a background model on moving platforms (intelligent cars, UAVs, etc.) is a challenging task due camera motion when images are acquired. In this paper, we propose a method to support instabilities caused by aerial images fusing spatial and temporal information about image motion. We used frame difference as first approximation, then age of pixels is estimated. This latter gives us an invariability level of a pixel over time. Gradient direction of ages and an adaptive weight are used to reduce impact from camera motion on background modelling. We tested our proposed method simulating several conditions that impair aerial image acquisition such as intentional and unintentional camera motion. Experimental results show improved performance compared to algorithms GMM and KDE.