Local Stereo Matching Based on Support Weight With Motion Flow for Dynamic Scene

Stereo matching is one of the most important and challenging subjects in the field of stereo vision. The disparity obtained in stereo matching can represent depth information in 3-D world to a great extent and shows great importance in stereo field. In general, stereo-matching methods primarily emphasize static image. However, the information provided by dynamic scene can be used fully and effectively to improve the results of stereo matching for dynamic scene, such as video sequences. In this paper, we propose a dynamic scene-based local stereo-matching algorithm which integrates a cost filter with motion flow of dynamic video sequences. In contrast to the existing local approaches, our algorithm puts forward a new computing model which fully considers motion information in dynamic video sequences and adds motion flow to calculate suitable support weight for accurately estimating disparity. Our algorithm can perform as an edge-preserving smoothing operator and shows improved behavior near the moving edges. The experimental results show that the proposed method achieves a better depth map and outperforms other local stereo-matching methods in disparity evaluation.