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 cooccurrence of some events in recorded traffic scenes. About 100-400 stereo frames (or 4-16 s of recording) are considered a basic sequence, which will be identified with one particular situation. Future testing is expected to be on data that report on hours of driving, and multiple hours of long video data may be segmented into basic sequences and classified into situations. This paper prepares for this expected development. This paper uses three different evaluation approaches (prediction error, synthesized sequences, and labeled sequences) for demonstrating ideas, difficulties, and possible ways in this future field of extensive performance tests in vision-based driver assistance, particularly for cases where the ground truth is not available. This paper shows that the complexity of real-world data does not support the identification of general rankings of correspondence techniques on sets of basic sequences that show different situations. It is suggested that correspondence techniques should adaptively be chosen in real time using some type of statistical situation classifiers.

No abstract

No abstract

This paper addresses the problem of detecting human faces in noisy images. We propose a method that includes a denoising preprocessing step, and a new face detection approach based on a novel extension of Haar-like features. Preprocessing of the input images is focused on the removal of different types of noise while preserving the phase data. For the face detection process, we introduce the concept of global and dynamic global Haar-like features, which are complementary to the well known classical Haar-like features. Matching dynamic global Haar-like features is faster than that of the traditional approach. Also, it does not increase the computational burden in the learning process. Experimental results obtained using images from the MIT-CMU dataset are promising in terms of detection rate and the false alarm rate in comparison with other competing algorithms.

Semi-global matching is a popular choice for applications where dense and robust stereo estimation is required and real-time performance is crucial. It therefore plays an important role in vision-based driver assistance systems. The strength of the algorithm comes from the integration of multiple 1D energy paths which are minimized along eight different directions across the image domain. The contribution of this paper is twofold. First, a thorough evaluation of stereo matching quality is performed when the number of accumulation paths is reduced. Second, an alteration of semi-global matching is proposed that operates only on half of the image domain without losing disparity resolution. The evaluation is performed on four real-world driving sequences of 400 frames each, as well as on 396 frames of a synthetic image sequence where sub-pixel accurate ground truth is available. Results indicate that a reduction of accumulation paths is a very good option to improve the run-time performance without losing significant quality, even on sub-pixel level. Furthermore, operating semiglobal matching only on half the image yields almost identical results to the corresponding full path integration. This approach yields the potential to further speed up the runtime and could also be exploited for other alterations of the algorithm.