Slanted Stixels: A way to represent steep streets

This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a Fully Convolutional Network (FCN), which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.Comment: Journal preprint (published in IJCV 2019: https://link.springer.com/article/10.1007/s11263-019-01226-9). arXiv admin note: text overlap with arXiv:1707.0539

Combining Appearance, Depth and Motion for Efficient Semantic Scene Understanding

Computer vision plays a central role in autonomous vehicle technology, because cameras are comparably cheap and capture rich information about the environment. In particular, object classes, i.e. whether a certain object is a pedestrian, cyclist or vehicle can be extracted very well based on image data. Environment perception in urban city centers is a highly challenging computer vision problem, as the environment is very complex and cluttered: road boundaries and markings, traffic signs and lights and many different kinds of objects that can mutually occlude each other need to be detected in real-time. Existing automotive vision systems do not easily scale to these requirements, because every problem or object class is treated independently. Scene labeling on the other hand, which assigns object class information to every pixel in the image, is the most promising approach to avoid this overhead by sharing extracted features across multiple classes. Compared to bounding box detectors, scene labeling additionally provides richer and denser information about the environment. However, most existing scene labeling methods require a large amount of computational resources, which makes them infeasible for real-time in-vehicle applications. In addition, in terms of bandwidth, a dense pixel-level representation is not ideal to transmit the perceived environment to other modules of an autonomous vehicle, such as localization or path planning. This dissertation addresses the scene labeling problem in an automotive context by constructing a scene labeling concept around the "Stixel World" model of Pfeiffer (2011), which compresses dense information about the environment into a set of small "sticks" that stand upright, perpendicular to the ground plane. This work provides the first extension of the existing Stixel formulation that takes into account learned dense pixel-level appearance features. In a second step, Stixels are used as primitive scene elements to build a highly efficient region-level labeling scheme. The last part of this dissertation finally proposes a model that combines both pixel-level and region-level scene labeling into a single model that yields state-of-the-art or better labeling accuracy and can be executed in real-time with typical camera refresh rates. This work further investigates how existing depth information, i.e. from a stereo camera, can help to improve labeling accuracy and reduce runtime

Efficient stereo matching and obstacle detection using edges in images from a moving vehicle

Fast and robust obstacle detection is a crucial task for autonomous mobile robots. Current approaches for obstacle detection in autonomous cars are based on the use of LIDAR or computer vision. In this thesis computer vision is selected due to its low-power and passive nature. This thesis proposes the use of edges in images to reduce the required storage and processing. Most current approaches are based on dense maps, where all the pixels in the image are used, but this places a heavy load on the storage and processing capacity of the system. This makes dense approaches unsuitable for embedded systems, for which only limited amounts of memory and processing power are available. This motivates us to use sparse maps based on the edges in an image. Typically edge pixels represent a small percentage of the input image yet they are able to represent most of the image semantics. In this thesis two approaches for the use of edges to obtain disparity maps are proposed and one approach for identifying obstacles given edge-based disparities. The first approach proposes a modification to the Census Transform in order to incorporate a similarity measure. This similarity measure behaves as a threshold on the gradient, resulting in the identification of high gradient areas. The identification of these high gradient areas helps to reduce the search space in an area-based stereo-matching approach. Additionally, the Complete Rank Transform is evaluated for the first time in the context of stereo-matching. An area-based local stereo-matching approach is used to evaluate and compare the performance of these pixel descriptors. The second approach proposes a new approach for the computation of edge-disparities. Instead of first detecting the edges and then reducing the search space, the proposed approach detects the edges and computes the disparities at the same time. The approach extends the fast and robust Edge Drawing edge detector to run simultaneously across the stereo pair. By doing this the number of matched pixels and the required operations are reduced as the descriptors and costs are only computed for a fraction of the edge pixels (anchor points). Then the image gradient is used to propagate the disparities from the matched anchor points along the gradients, resulting in one-voxel wide chains of 3D points with connectivity information. The third proposed algorithm takes as input edge-based disparity maps which are compact and yet retain the semantic representation of the captured scene. This approach estimates the ground plane, clusters the edges into individual obstacles and then computes the image stixels which allow the identification of the free and occupied space in the captured stereo-views. Previous approaches for the computation of stixels use dense disparity maps or occupancy grids. Moreover they are unable to identify more than one stixel per column, whereas our approach can. This means that it can identify partially occluded objects. The proposed approach is tested on a public-domain dataset. Results for accuracy and performance are presented. The obtained results show that by using image edges it is possible to reduce the required processing and storage while obtaining accuracies comparable to those obtained by dense approaches