Exploiting the Power of Stereo Confidences

Applications based on stereo vision are becoming increasingly common, ranging from gaming over robotics to driver assistance. While stereo algorithms have been investigated heavily both on the pixel and the application level, far less attention has been dedicated to the use of stereo confidence cues. Mostly, a threshold is applied to the confidence values for further processing, which is essentially a sparsified disparity map. This is straightforward but it does not take full advantage of the available information. In this paper, we make full use of the stereo confidence cues by propagating all confidence values along with the measured disparities in a Bayesian manner. Before using this information, a mapping from confidence values to disparity outlier probability rate is performed based on gathered disparity statistics from labeled video data. We present an extension of the so called Stixel World, a generic 3D intermediate representation that can serve as input for many of the applications mentioned above. This scheme is modified to directly exploit stereo confidence cues in the underlying sensor model during a maximum a posteriori estimation process. The effectiveness of this step is verified in an in-depth evaluation on a large real-world traffic data base of which parts are made publicly available. We show that using stereo confidence cues allows both reducing the number of false object detections by a factor of six while keeping the detection rate at a near constant level.