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Author |
Antonio Lopez; David Vazquez; Gabriel Villalonga |
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Title |
Data for Training Models, Domain Adaptation |
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2018 |
Publication |
Intelligent Vehicles. Enabling Technologies and Future Developments |
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395–436 |
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Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data |
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Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection. |
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ADAS; 600.118 |
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Admin @ si @ LVV2018 |
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3047 |
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Author |
Antonio Lopez; Jiaolong Xu; Jose L. Gomez; David Vazquez; German Ros |
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Title |
From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example |
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Book Chapter |
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Year |
2017 |
Publication |
Domain Adaptation in Computer Vision Applications |
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13 |
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243-258 |
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Domain Adaptation |
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Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter we revisit the DA of a deformable part-based model (DPM) as an exemplifying case of virtual- to-real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as: how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world. |
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Springer |
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Gabriela Csurka |
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ADAS; 600.085; 601.223; 600.076; 600.118 |
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ADAS @ adas @ LXG2017 |
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2872 |
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Jose M. Armingol; Jorge Alfonso; Nourdine Aliane; Miguel Clavijo; Sergio Campos-Cordobes; Arturo de la Escalera; Javier del Ser; Javier Fernandez; Fernando Garcia; Felipe Jimenez; Antonio Lopez; Mario Mata |
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Title |
Environmental Perception for Intelligent Vehicles |
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2018 |
Publication |
Intelligent Vehicles. Enabling Technologies and Future Developments |
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23–101 |
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Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles |
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Environmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems. |
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ADAS; 600.118 |
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Admin @ si @AAA2018 |
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3046 |
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Author |
David Geronimo; Antonio Lopez |
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Title |
Vision-based Pedestrian Protection Systems for Intelligent Vehicles |
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2014 |
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SpringerBriefs in Computer Science |
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1-114 |
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Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users |
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Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented. |
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Springer Briefs in Computer Vision |
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978-1-4614-7986-4 |
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ADAS; 600.076 |
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GeL2014 |
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2325 |
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Author |
David Geronimo; David Vazquez; Arturo de la Escalera |
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Title |
Vision-Based Advanced Driver Assistance Systems |
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2017 |
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Computer Vision in Vehicle Technology: Land, Sea, and Air |
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ADAS; Autonomous Driving |
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ADAS; 600.118 |
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ADAS @ adas @ GVE2017 |
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2881 |
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Author |
Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution texture classification of ceramic tiles. |
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1999 |
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Recent Research developments in optical engineering, Research Signpost, 2: 213–228 |
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India |
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ADAS;CIC |
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ADAS @ adas @ LBV1999b |
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45 |
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Author |
Ricardo Toledo |
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Title |
Cardiac workstation and dynamic model to assist in coronary tree analysis. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Petia Radeva;JuanJose Villanueva |
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Admin @ si @ Tol2001 |
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166 |
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Author |
Antonio Lopez |
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Title |
Multilocal Methods for Ridge and Valley Delineation in Image Analysis. |
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2000 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Joan Serrat |
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ADAS @ adas @ Lop2000 |
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174 |
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Felipe Lumbreras |
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Title |
Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. |
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2001 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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ADAS @ adas @ Lum2001 |
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188 |
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Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
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Title |
Advances in Vision-Based Human Body Modeling |
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2004 |
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3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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1-26 |
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N. Sarris and M. Strintzis. |
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1-59140-299-9 |
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ADAS @ adas @ SAG2004a |
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458 |
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