Abstract

Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicle’s departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm. Document type: Part of book or chapter of book

Abstract

The increasing importance of outdoor applications such as driver assistance systems or video surveillance tasks has recently triggered the development of optical flow methods that aim at performing robustly under uncontrolled illumination. Most of these methods are based on patch-based features such as the normalized cross correlation, the census transform or the rank transform. They achieve their robustness by locally discarding both absolute brightness and contrast. In this paper, we follow an alternative strategy: Instead of discarding potentially important image information, we propose a novel variational model that jointly estimates both illumination changes and optical flow. The key idea is to parametrize the illumination changes in terms of basis functions that are learned from training data. While such basis functions allow for a meaningful representation of illumination effects, they also help to distinguish real illumination changes from motion-induced brightness variations if supplemented by additional smoothness constraints. Experiments on the KITTI benchmark show the clear benefits of our approach. They do not only demonstrate that it is possible to obtain meaningful basis functions, they also show state-of-the-art results for robust optical flow estimation. Document type: Part of book or chapter of book

Abstract

An important requirement of a robust traffic engineering solution is insensitivity to changes, be they in the form of traffic fluctuations or changes in the network topology because of link failures. In this paper we focus on developing a fast and effective technique to compute traffic engineering solutions for Interior Gateway Protocol (IGPs) environments that are robust to link failures in the logical topology. The routing and packet forwarding decisions for IGPs is primarily governed by link weights. Our focus is on computing a single set of link weights for a traffic engineering instance that performs well over all single logical link failures. Such types of failures, although usually not long lasting, of the order of tens of minutes, can occur with high enough frequency, of the order of several a day, to significantly affect network performance. The relatively short duration of such failures coupled with issues of computational complexity and convergence time due to the size of current day networks discourage adaptive reactions to such events. Consequently, it is desirable to a priori compute a routing solution that performs well in all such scenarios. Through computational evaluations we demonstrate that our technique yields link weights that perform well over all single link failures and also scales well, in terms of computational complexity, with the size of the network. Document type: Part of book or chapter of book

Abstract

Many scientific applications deal with data from a multitude of different sources, e.g., measurements, imaging and simulations. Each source provides an additional perspective on the phenomenon of interest, but also comes with specific limitations, e.g. regarding accuracy, spatial and temporal availability. Effectively combining and analyzing such multimodal and partially incomplete data of limited accuracy in an integrated way is challenging. In this work, we outline an approach for an integrated analysis and visualization of the atmospheric impact of volcano eruptions. The data sets comprise observation and imaging data from satellites as well as results from numerical particle simulations. To analyze the clouds from the volcano eruption in the spatiotemporal domain we apply topological methods. We show that topology-related extremal structures of the data support clustering and comparison. We further discuss the robustness of those methods with respect to different properties of the data and different parameter setups. Finally we outline open challenges for the effective integrated visualization using topological methods. Document type: Part of book or chapter of book

Abstract

Intra-domain traffic engineering is essential for the operation of an Internet Service Provider. Demand-oblivious routing [2] promises excellent performance guarantee with changing and uncertain traffic demands. However, it is difficult to implement it. We investigate an efficient and deployable implementation of oblivious routing. We study its performance by both numerical experiments and simulation. The performance study shows that the multipath implementation achieves a close approximation to oblivious routing [2], especially when approximate knowledge of traffic is available. The study also shows its robustness under varying traffic demands, link failures and an adversary attack. Its performance is excellent even with a 100% error in traffic estimation. Document type: Part of book or chapter of book

Abstract

We study the digitisation of dense-time behaviours of timed processes, and show how this leads to exact verification methods for a large class of dense-time specifications. These specifications are all closed under inverse digitisation, a robustness property first introduced by Henzinger, Manna, and Pnueli (on timed traces), and extended here to timed failures, enabling us to consider liveness issues in addition to safety properties. We discuss a corresponding model checking algorithm and show that, in many cases, automated verification of such dense-time specifications can in fact be directly performed on the model checker FDR (a commercial product of Formal Systems (Europe) Ltd.). We illustrate this with a small case study (the railway level crossing problem). Finally, we show that integral--or digitised--behaviours are fully abstract with respect to specifications closed under inverse digitisation, and relate this to the efficiency of our model checking algorithm. Document type: Part of book or chapter of book

Abstract

Today's stereo vision algorithms and computing technology allow real-time 3D data analysis, for example for driver assistance systems. A recently developed Semi-Global Matching (SGM) approach by H. Hirschmuller became a popular choice due to performance and robustness. This paper evaluates different parameter settings for SGM, and its main contribution consists in suggesting to include a second order prior into the smoothness term of the energy function. It also proposes and tests a new cost function for SGM. Furthermore, some preprocessing (edge images) proved to be of great value for improving SGM stereo results on real-world sequences, as previously already shown by S. Guan and R. Klette for belief propagation. There is also a performance gain for engineered stereo data (e.g.) as currently used on the Middlebury stereo website. However, the fact that results are not as impressive as on the .enpeda.. sequences indicates that optimizing for engineered data does not neccessarily improve real world stereo data analysis. Document type: Part of book or chapter of book

Abstract

International audience To sustain the continuously increasing air traffic demand, the future air traffic management system will rely on a so-called trajectory based operations concept that will increase air traffic capacity by reducing the controllers’ workload. This will be achieved by transferring tactical conflict detection and resolution tasks to the strategic planning phase. In this future air traffic management paradigm context, this paper presents a methodology to address such trajectory planning at nation-wide and continent scale. The pro-posed methodology aims at minimizing the global interaction between aircraft trajectories by allocating alternative departure times, alternative horizontal flight paths, and alternative flight levels to the trajectories involved in the interaction. To improve robustness of the strategic trajectory planning, un-certainty of aircraft position and aircraft arrival time to any given position on the trajectory are considered. This paper presents a mathematical formulation of this strategic trajectory planning problem leading to a mixed-integer optimization problem, whose objective function relies on the new concept of interaction between trajectories. A computationally efficient algorithm to compute interaction between trajectories for large-scale applications is presented and implemented. Resolution method based on hybrid-metaheuristic algorithm have been developed to solve the above large-scale optimization problems. Finally, the overall methodology is implemented and tested with real air traffic data taking into account uncertainty over the French and the European airspace, involving more than 30,000 trajectories. Conflict-free and robust 4D trajectory planning are produced within computational time acceptable for the operation context, which shows the viability of the approach. Document type: Part of book or chapter of book

Abstract

International audience; The rise of virtual and augmented reality fuels an increased need for contents suitable to these new technologies including 3D contents obtained from real scenes. We consider in this paper the problem of 3D shape reconstruction from multi-view RGB images. We investigate the ability of learning-based strategies to effectively benefit the reconstruction of arbitrary shapes with improved precision and robustness. We especially target real life performance capture, containing complex surface details that are difficult to recover with existing approaches. A key step in the multi-view reconstruction pipeline lies in the search for matching features between viewpoints in order to infer depth information. We propose to cast the matching on a 3D receptive field along viewing lines and to learn a multi-view photoconsistency measure for that purpose. The intuition is that deep networks have the ability to learn local photometric configurations in a broad way, even with respect to different orientations along various viewing lines of the same surface point. Our results demonstrate this ability, showing that a CNN, trained on a standard static dataset, can help recover surface details on dynamic scenes that are not perceived by traditional 2D feature based methods. Our evaluation also shows that our solution compares on par to state of the art reconstruction pipelines on standard evaluation datasets, while yielding significantly better results and generalization with realistic performance capture data. Document type: Part of book or chapter of book