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This paper presents a technique for estimating the three-dimensional velocity vector field that describes the motion of each visible scene point (scene flow). The technique presented uses two consecutive image pairs from a stereo sequence. The main contribution is to decouple the position and velocity estimation steps, and to estimate dense velocities using(More)
Building upon recent developments in optical flow and stereo matching estimation, we propose a varia-tional framework for the estimation of stereoscopic scene flow, i.e., the motion of points in the three-dimensional world from stereo image sequences. The proposed algorithm takes into account image pairs from two consecutive times and computes both depth(More)
Performance evaluation of stereo or motion analysis techniques is commonly done either on synthetic data where the ground truth can be calculated from ray-tracing principals, or on engineered data where ground truth is easy to estimate. Furthermore, these scenes are usually only shown in a very short sequence of images. This paper shows why synthetic scenes(More)
This paper presents a method for the computation of free space in complex traffic scenarios. Dynamic depth information is estimated by integrating stereo disparity images over time. Disparity and disparity speed are computed pixel-wise with Kalman filters. The stereo information is used to compute stochastic occupancy grids. Dynamic programming on a(More)
Disparity map generation is a significant component of vision-based driver assistance systems. This paper describes an efficient implementation of a belief propagation algorithm on a graphics card (GPU) using CUDA (Compute Uniform Device Architecture) that can be used to speed up stereo image processing by between 30 and 250 times. For evaluation purposes,(More)
This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereo-scopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is(More)
—This paper discusses options for testing correspondence algorithms in stereo or motion analysis that are designed or considered for vision-based driver assistance. It introduces a globally available database, with a main focus on testing on video sequences of real-world data. We suggest the classification of recorded video data into situations defined by a(More)
Lane detection is a significant component of driver assistance systems. Highway-based lane departure warning solutions are in the market since the mid-1990s. However, improving and generalizing vision-based lane detection remains to be a challenging task until recently. Among various lane detection methods developed, strong lane models, based on the global(More)
This paper presents an approach to test stereo algorithms against long stereo sequences (say, 100+ image pairs). Stereo sequences of this length have not been quantitatively evaluated in the past, even though they are the input data of a vision-based driver assistance system. Using stereo sequences allows one to exploit the temporal information, which is,(More)
Intelligent vehicle systems need to distinguish which objects are moving and which are static. A static concrete wall lying in the path of a vehicle should be treated differently than a truck moving in front of the vehicle. This paper proposes a new algorithm that addresses this problem, by providing dense dynamic depth information, while coping with(More)