Sandino Morales

Performance of Correspondence Algorithms in Vision-Based Driver Assistance Using an Online Image Sequence Database

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.

A Third Eye for Performance Evaluation in Stereo Sequence Analysis

Prediction errors are commonly used when analyzing the performance of a multi-camera stereo system using at least three cameras. This paper discusses this methodology for performance evaluation for the first time on long stereo sequences (in the context of vision-based driver assistance systems). Three cameras are calibrated in an ego-vehicle, and prediction error analysis is performed on recorded stereo sequences. They are evaluated using various common stereo matching algorithms, such as belief propagation, dynamic programming, semi-global matching, or graph cut. Performance is evaluated on both synthetic and real data.

Robustness evaluation of stereo algorithms on long stereo sequences

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, in general, not well used currently. The presented approach focuses on evaluating the robustness of algorithms against differing noise parameters (Gaussian noise, brightness differences, and blurring).

Ground truth evaluation of stereo algorithms for real world applications

Current stereo algorithms are capable to calculate accurate (as defined, e.g., by needs in vision-based driver assistance) dense disparity maps in real time. They have become the source of three-dimensional data for several indoor and outdoor applications. However, ground truthbased evaluation of such algorithms has been typically limited to data sets generated indoors in laboratories. In this paper we present a new approach to evaluate stereo algorithms using ground-truth over real world data sets. Ground truth is generated using range measurements acquired with a high-end laser range-finder. For evaluating as many points as possible in a given disparity map, we use two evaluation approaches: A direct comparison for those pixels with available range data, and a confidence measure for the remaining pixels.

A Study on Stereo and Motion Data Accuracy for a Moving Platform

Stereo and motion analysis are potential techniques for providing information for control or assistance systems in various robotics or driver assistance applications. This paper evaluates the performance of several stereo and motion algorithms over a long synthetic sequence (100 stereo pairs). Such an evaluation of low-level computer vision algorithms is necessary, as moving platforms are being used for image analysis in a wide area of applications. In this paper algorithms are evaluated with respect to robustness by modifying the test sequence with various types of realistic noise. The novelty of this paper is comparing top performing algorithms on a long sequence of images, taken from a moving platform.

Ground truth evaluation of the Stixel representation using laser scanners

Modern real-time dense stereo vision provides precise depth information for nearly every pixel of an image, indicating stereo cameras as a key sensor for future vehicle safety systems. Efficient analysis of this large amount of data by different tasks running in parallel asks for a medium level representation that decouples application specific analysis from low-level vision. Recently, the so called “Stixel World” has been proposed. It models the objects in the scene, implicitly separates them from the ground plane, encodes the freespace to maneuver and thus represents the scene in a highly compact manner that supports different recognition tasks efficiently. The potential of this new representation depends on the accuracy that can be achieved. Therefore, this paper analyzes the precision of this representation using a high performance laser scanner as reference sensor. The statistical analysis confirms the high accuracy as expected from visual inspection.

Illumination invariant cost functions in semi-global matching

The paper evaluates three categories of similarity measures: ordering-based (census), gradient-based, and illumination-based cost functions. The performance of those functions is evaluated especially with respect to illumination changes using two different sets of data, also including real world driving sequences of hundreds of stereo frames with strong illumination differences. The overall result is that there are cost functions in all three categories that can perform well on a quantitative and qualitative level. This leads to the assumption that those cost functions are in fact closely related at a qualitative level, and we provide our explanation.

Advance in vision-based driver assistance

Vision-based driver assistance is an active safety measure currently under development in various car companies and research institutes worldwide. The paper informs about related activities at The University of Auckland, focussing on stereo vision, performance evaluation, provided test data, and currently developed components.

A comparative study of stereo-matching algorithms for road-modeling in the presence of windscreen wipers

In this study we examine three road-modeling methods, which we evaluate on seven stereo matching algorithms. The road-modeling methods we consider are a B-spline modeling technique based on region-growing and two versions of the popular v-disparity approach. The used stereo algorithms are variations or different parameterizations of belief propagation, graph cut and semi-global matching.

Generalised residual images' effect on illumination artifact removal for correspondence algorithms

The intensity (grey value) consistency of image pixels in a sequence or stereo camera setup is of central importance to numerous computer vision applications. Most stereo matching and optical flow algorithms minimise an energy function composed of a data term and a regularity or smoothing term. To date, well performing methods rely on the intensity consistency of the image pixel values to model the data term. Such a simple model fails if the illumination is (even slightly) different between the input images. Amongst other situations, this may happen due to background illumination change over the sequence, different reflectivity of a surface, vignetting, or shading effects. In this paper, we investigate the removal of illumination artifacts and show that generalised residual images substantially improve the accuracy of correspondence algorithms. In particular, we motivate the concept of residual images and show two evaluation approaches using either ground truth correspondence fields (for stereo matching and optical flow algorithms) or errors based on a predicted view (for stereo matching algorithms).