A prediction-correction approach for real-time optical flow computation using stereo

Abstract

Estimating the optical flow robustly in real-time is still a challenging issue as revealed by current KITTI benchmarks. We propose an original two-step method for fast and performant optical flow estimation from stereo vision. The first step is the prediction of the flow due to the ego-motion, efficiently conducted by stereo-matching and visual odometry. The correction step estimates the motion of mobile objects. Algorithmic choices are justified by empirical studies on real datasets. Our method achieves framerate processing on images of realistic size, and provides results comparable or better than methods having computation times one or two orders of magnitude higher.