This paper presents a motion detection approach based on a combination of dense optical flow and 3D stereo reconstruction. Our motion detection is not based on predefined templates, providing a generic framework suitable for a broad range of applications such as situation awareness. The approach estimates the likelihood of pixels motion from the fusion of dense optical flow and dense depth information estimated from a stereo camera. Temporal consistency is incorporated by tracking moving objects across consecutive images. The proposed algorithm is validated with publicly available datasets. The consistent results across different scenarios demonstrate the robustness of our framework, presenting an average detection rate of 92%.