Semi-Global Matching (SGM) is arguably one of the most popular algorithms for real-time stereo vision. It is already employed in mass production vehicles today. Thinking of applications in intelligent vehicles (and fully autonomous vehicles in the long term), we aim at further improving SGM regarding its accuracy. In this study, we propose a straight-forward extension of the algorithm's parametrization. We consider individual penalties for different path orientations, weighted integration of paths, and penalties depending on intensity gradients. In order to tune all parameters, we applied evolutionary optimization. For a more efficient offline optimization and evaluation, we implemented SGM on graphics hardware. We describe the implementation using CUDA in detail. For our experiments, we consider two publicly available datasets: the popular Middlebury benchmark as well as a synthetic sequence from the .enpeda. project. The proposed extensions significantly improve the performance of SGM. The number of incorrect disparities was reduced by up to 27.5 % compared to the original approach, while the runtime was not increased.

Real-time detection of traffic signs, the task of pinpointing a traffic sign's location in natural images, is a challenging computer vision task of high industrial relevance. Various algorithms have been proposed, and advanced driver assistance systems supporting detection and recognition of traffic signs have reached the market. Despite the many competing approaches, there is no clear consensus on what the state-of-the-art in this field is. This can be accounted to the lack of comprehensive, unbiased comparisons of those methods. We aim at closing this gap by the “German Traffic Sign Detection Benchmark” presented as a competition at IJCNN 2013 (International Joint Conference on Neural Networks). We introduce a real-world benchmark data set for traffic sign detection together with carefully chosen evaluation metrics, baseline results, and a web-interface for comparing approaches. In our evaluation, we separate sign detection from classification, but still measure the performance on relevant categories of signs to allow for benchmarking specialized solutions. The considered baseline algorithms represent some of the most popular detection approaches such as the Viola-Jones detector based on Haar features and a linear classifier relying on HOG descriptors. Further, a recently proposed problem-specific algorithm exploiting shape and color in a model-based Houghlike voting scheme is evaluated. Finally, we present the best-performing algorithms of the IJCNN competition.

This Special Issue, comprised of four articles, is dedicated to recent developments in the application of machine learning algorithms to traffic sign recognition. The recognition of traffic signs is a challenging real-world problem, which is relevant for intelligent transportation systems participating in traffic environments. It poses a multicategory classification problem with unbalanced class frequencies. Traffic signs show a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. However, some subsets of classes are very similar to each other (e.g., speed limit signs). In addition to these interclass differences and similarities, the classifier has to cope with large variations in visual appearances due to illumination changes, partial occlusions, rotations, weather conditions, etc.

The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.

In this paper, we present a real-time video-based face recognition system. The developed system identifies subjects while they are entering a room. This application scenario poses many challenges. Continuous, uncontrolled variations of facial appearance due to illumination, pose, expression, and occlusion of non-cooperative subjects need to be handled to allow for successful recognition. In order to achieve this, the system first detects and tracks the eyes for proper registration. The registered faces are then individually classified by a local appearance-based face recognition algorithm. The obtained confidence scores from each classification are progressively combined to provide the identity estimate of the entire sequence. We introduce three different measures to weight the contribution of each individual frame to the overall classification decision. They are distance-to-model (DTM), distance-to-second-closest (DT2ND), and their combination. We have conducted closed-set and open-set identification experiments on a database of 41 subjects. The experimental results show that the proposed system is able to reach high correct recognition rates. Besides, it is able to perform facial feature and face detection, tracking, and recognition in real-time.

In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each person’s distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images.

In this paper, we present the classification sub-system of a real-time video-based face identification system which recognizes people entering through the door of a laboratory. Since the subjects are not asked to cooperate with the system but are allowed to behave naturally, this application scenario poses many challenges. Continuous, uncontrolled variations of facial appearance due to illumination, pose, expression, and occlusion need to be handled to allow for successful recognition. Faces are classified by a local appearance-based face recognition algorithm. The obtained confidence scores from each classification are progressively combined to provide the identity estimate of the entire sequence. We introduce three different measures to weight the contribution of each individual frame to the overall classification decision. They are distance-to-model (DTM), distance-to-second-closest (DT2ND), and their combination. Both a k-nearest neighbor approach and a set of Gaussian mixtures are evaluated to produce individual frame scores. We have conducted closed set and open set identification experiments on a database of 41 subjects. The experimental results show that the proposed system is able to reach high correct recognition rates in a difficult scenario.

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