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Computationally efficient moving object detection and depth estimation from astereo camera is an extremely useful tool for many computer visionapplications, including robotics and autonomous driving. In this paper we showhow moving objects can be densely detected by estimating disparity using analgorithm that improves complexity and accuracy of stereo matching by relyingon information from previous frames. The main idea behind this approach is thatby 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 currentframe disparity estimation, subsequently also detecting moving objects in thescene. For each pixel we run a Kalman filter that recursively fuses thedisparity prediction and reduced space semi-global matching (SGM) measurements.The proposed algorithm has been implemented and optimized using streamingsingle instruction multiple data instruction set and multi-threading.Furthermore, in order to estimate the process and measurement noise as reliablyas possible, we conduct extensive experiments on the KITTI suite using theground truth obtained by the 3D laser range sensor. Concerning disparityestimation, compared to the OpenCV SGM implementation, the proposed methodyields improvement on the KITTI dataset sequences in terms of both speed andaccuracy.

Computationally efficient dense moving object detection based on reduced space disparity estimation