  Computationally efficient moving object detection and depth estimation from a
stereo camera is an extremely useful tool for many computer vision
applications, including robotics and autonomous driving. In this paper we show
how moving objects can be densely detected by estimating disparity using an
algorithm that improves complexity and accuracy of stereo matching by relying
on information from previous frames. The main idea behind this approach is that
by using the ego-motion estimation and the disparity map of the previous frame,
we can set a prior base that enables us to reduce the complexity of the current
frame disparity estimation, subsequently also detecting moving objects in the
scene. For each pixel we run a Kalman filter that recursively fuses the
disparity prediction and reduced space semi-global matching (SGM) measurements.
The proposed algorithm has been implemented and optimized using streaming
single instruction multiple data instruction set and multi-threading.
Furthermore, in order to estimate the process and measurement noise as reliably
as possible, we conduct extensive experiments on the KITTI suite using the
ground truth obtained by the 3D laser range sensor. Concerning disparity
estimation, compared to the OpenCV SGM implementation, the proposed method
yields improvement on the KITTI dataset sequences in terms of both speed and
accuracy.
