Stixel calculations are commonly based on binocular vision; these calculations map millions of pixel disparities into a few hundred stixels. Depending on applied stereo vision, this binocular approach is sometimes incapable of dealing with low-textured road information or noisy data. The main objective of this work is to propose a more reliable approach to calculating stixels by incorporating laser scanners (i.e., LIDAR). We show that this supports more efficient and robust 3D point representations, even if only integrating monocular vision into the LIDAR-based approach for generating monocular stixels. Experimental results show a more accurate (by 15.4 %) stixel detection rate when the LIDAR-guided monocular configuration is used compared to a conventional binocular approach.

This paper highlights the role of ground manifold modeling for stixel calculations; stixels are medium-level data representations used for the development of computer vision modules for self-driving cars. By using single-disparity maps and simplifying ground manifold models, calculated stixels may suffer from noise, inconsistency, and false-detection rates for obstacles, especially in challenging datasets. Stixel calculations can be improved with respect to accuracy and robustness by using more adaptive ground manifold approximations. A comparative study of stixel results, obtained for different ground-manifold models (e.g., plane-fitting, line-fitting in $v$ -disparities or polynomial approximation, and graph cut), defines the main part of this paper. This paper also considers the use of trinocular stereo vision and shows that this provides options to enhance stixel results, compared with the binocular recording. Comprehensive experiments are performed on two publicly available challenging datasets. We also use a novel way for comparing calculated stixels with ground truth. We compare depth information, as given by extracted stixels, with ground-truth depth, provided by depth measurements using a highly accurate LiDAR range sensor (as available in one of the public datasets). We evaluate the accuracy of four different ground-manifold methods. The experimental results also include quantitative evaluations of the tradeoff between accuracy and run time. As a result, the proposed trinocular recording together with graph-cut estimation of ground manifolds appears to be a recommended way, also considering challenging weather and lighting conditions.

We propose the use of a reliable confidence map for multi-layer stixel segmentation; our confidence map uses a calibrated collinear trinocular vision model. It is generated from three conjugate synchronized stereo images for evaluating the consistency of disparity values. The evaluation measure is referred to as transitivity error in disparity space. Multi-layer stixels are commonly generated from a single disparity map which make them merely dependent on the applied stereo matcher. A multi-map fusion is proposed to achieve more reliable stixel segmentation for disparity values. Moreover, another advantage of our work is to provide a new and effective ground-detection technique (ground-manifold detection) which benefits from the confidence map. Experimental results show a significant improvement on average of 12.6% using our method compared with conventional stixels detected by binocular vision only.

This paper presents a stereo-based method towards robust identification of obstacle heights using stixels. The aim is to ensure a very low false detection rate for obstacles in video data recorded in vehicles. Basically, frames are segmented into ground manifold and obstacles (approximated by stixels). To robustly estimate the obstacle height, our method leverages the height of stixels by fusing color information, represented by a saliency map, with disparity information, represented by a membership map. Experiments illustrate the accuracy of the proposed method using common benchmark datasets. The proposed method outperforms previously published methods on the used datasets; in particular, (as demonstrated in this brief paper) it performs superior compared to base-line stixel calculation in cases of challenging scene conditions.

This paper presents a stereo-based method for robust vertical road profile detection. The aim is to ensure a very low false-detection rate for obstacles in challenging datasets. Basically, frames are segmented into ground manifold and obstacles. To robustly estimate a vertical road profile, our method applies cuts through a v-disparity matrix along columns to achieve a minimal cost specified by the matrix itself while maintaining a desired smoothness; the minimization is based on the Viterbi algorithm, a dynamic programming technique. Experiments illustrate the performance of the proposed method using available datasets. Results show that the proposed method outperforms two previously published methods on the used datasets. In particular, its performance is superior to the others in cases of challenging weather conditions.

Stixel-based segmentation is specifically designed towards obstacle detection which combines road surface estimation in traffic scenes, stixel calculations, and stixel clustering. Stixels are defined by observed height above road surface. Road surfaces (ground manifolds) are represented by using an occupancy grid map. Stixel-based segmentation may improve the accuracy of real-time obstacle detection, especially if adaptive to changes in ground manifolds (e.g. with respect to non-planar road geometry). In this paper, we propose the use of a polynomial curve fitting algorithm based on the v-disparity space for ground manifold estimation. This is beneficial for two reasons. First, the coordinate space has inherently finite boundaries, which is useful when working with probability densities. Second, it leads to reduced computation time. We combine height segmentation and improved ground manifold algorithms together for stixel extraction. Our experimental results show a significant improvement in the accuracy of the ground manifold detection (an 8% improvement) compared to occupancy-grid mapping methods.

We present a novel method for stixel construction using a calibrated collinear trinocular vision system. Our method takes three conjugate stereo images at the same time to measure the consistency of disparity values by means of the transitivity error in disparity space. Unlike previous stixel estimation methods that are built based on a single disparity map, our proposed method introduces a multi-map fusion technique to obtain more robust stixel calculations. We also apply a polynomial curve fitting approach to detect an accurate road manifold, using the v-disparity space which is built based on a confidence map, which further supports accurate stixel calculation. Comparing the depth information from the extracted stixels (using stixel maps) with depth measurements obtained from a highly accurate LiDAR range sensor, we evaluate the accuracy of the proposed method. Experimental results indicate a significant improvement of 13.6% in the accuracy of stixel detection compared to conventional binocular vision.

Autonomous on-road vehicles or vision-based driver assistance benefit from free-space analysis. This paper evaluates the accuracy of free-space detection in stereo and monocular vision on KITTI benchmark data. Such an evaluation of low-level computer vision algorithms is, for example, also necessary as free-space analysis is recently becoming an important module for designing vehicle test beds. The novelty of this paper is defined by comparing a designed monocular algorithm with a selected binocular algorithm on long sequences of images, taken for different road profiles and lighting conditions. The results demonstrate potentials of monocular vision as applicable, for example, if using mobile devices only. Furthermore, this paper extends the detection of a lower envelop in a v-disparity image, usually done by estimating a straight line or B-spline, by using polynomial curve fitting.

Over the past several years, it can be observed there are many demands to enhance and develop e-learning system which is considered as highly desirable in many institutes and universities. One of the essential parts of e-learning is the assessments in order test student’s continuous mutual development who use e-learning. The current electronic examination systems faced certain limitations in privacy and system configuration. Therefore, main objective of this study is to employ Bluetooth as an alternative medium to transfer questions and answers between lecturer and student programs which both will be designed using C# language. The proposed research Bluetooth Assessment System (BAS) is divided into two programs: Student Program (SP) and Lecturer Program (LP), each one of them has its own features and functions. This paper presents the system architecture and computational algorithms for the proposed system, and also illustrates its use for universities and institutions.

Multimedia authoring tools are deservedly popular for their potentially high effective productivity and usefulness for prototyping. However the development can cause problems if these authoring tools are not properly evaluated and used. In this work we employed ToolBook® to develop several interactive multimedia coursewares and evaluated its usefullness. The results of the study showed that ToolBook has both strength and weaknesses despite providing good elements for authoring such as navigation, interactivity, features, media support, deployment options and assessments.

Mind mapping is an illustration approach that has been used to facilitate the process of learning and the visualization of concepts better. In these days the utilization of mind mapping approaches in education have been increased significantly due to its success and its positive effect on students understanding especially in the Geography subjects which concepts are often difficult to visualize and to observe. In this paper we propose the use of multimedia and CAL applied in mind mapping in the geography of Malaysia by designing interactive patterns. An evaluation was conducted in some elementary schools to show the result of using this courseware in the education section. The project consists of a number of interactive pages designed to help Malaysian students aged between (11-13) years old, to visualize their understanding on the principles of geography in general, and the geography of Malaysia specifically, using their native language (Malay language).