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Author | Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez | ||||
Title | Geographic Information for vision-based Road Detection | Type | Conference Article | ||
Year | 2010 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
Volume | Issue | Pages | 621–626 | ||
Keywords | road detection | ||||
Abstract | Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach. | ||||
Address | San Diego; CA; USA | ||||
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Area | Expedition | Conference | IV | ||
Notes | ADAS;ISE | Approved | no | ||
Call Number | ADAS @ adas @ ALG2010 | Serial | 1428 | ||
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Author | Jose Manuel Alvarez; Felipe Lumbreras; Antonio Lopez; Theo Gevers | ||||
Title | Understanding Road Scenes using Visual Cues | Type | Miscellaneous | ||
Year | 2012 | Publication | European Conference on Computer Vision | Abbreviated Journal | |
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Abstract | DEMO | ||||
Address | Florence; Italy | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ ALL2012 | Serial | 2795 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras | ||||
Title | Combining Priors, Appearance and Context for Road Detection | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 15 | Issue | 3 | Pages | 1168-1178 |
Keywords | Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout | ||||
Abstract | Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios. |
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Publisher | Place of Publication | Editor | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | ||
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ISSN | 1524-9050 | ISBN | Medium | ||
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Notes | ADAS; 600.076;ISE | Approved | no | ||
Call Number | Admin @ si @ ALG2014 | Serial | 2501 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich | ||||
Title | Shadow Resistant Road Segmentation from a Mobile Monocular System | Type | Conference Article | ||
Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 | Abbreviated Journal | |
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Keywords | road detection | ||||
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Address | Gerona (Spain) | ||||
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Notes | ADAS;CIC | Approved | no | ||
Call Number | ADAS @ adas @ ALB2007 | Serial | 943 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich | ||||
Title | Illuminant Invariant Model-Based Road Segmentation | Type | Conference Article | ||
Year | 2008 | Publication | IEEE Intelligent Vehicles Symposium, | Abbreviated Journal | |
Volume | Issue | Pages | 1155–1180 | ||
Keywords | road detection | ||||
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Address | Eindhoven (The Netherlands) | ||||
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Notes | ADAS;CIC | Approved | no | ||
Call Number | ADAS @ adas @ ALB2008 | Serial | 1045 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez | ||||
Title | Novel Index for Objective Evaluation of Road Detection Algorithms | Type | Conference Article | ||
Year | 2008 | Publication | Intelligent Transportation Systems. 11th International IEEE Conference on, | Abbreviated Journal | |
Volume | Issue | Pages | 815–820 | ||
Keywords | road detection | ||||
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Address | Beijing (Xina) | ||||
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Area | Expedition | Conference | ITSC | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ AlL2008 | Serial | 1074 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez | ||||
Title | Model-based road detection using shadowless features and on-line learning | Type | Miscellaneous | ||
Year | 2009 | Publication | BMVA one–day technical meeting on vision for automotive applications | Abbreviated Journal | |
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Keywords | road detection | ||||
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Address | London, UK | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ AlA2009 | Serial | 1272 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez | ||||
Title | Road Detection Based on Illuminant Invariance | Type | Journal Article | ||
Year | 2011 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 12 | Issue | 1 | Pages | 184-193 |
Keywords | road detection | ||||
Abstract | By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms. | ||||
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Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ AlL2011 | Serial | 1456 | ||
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Author | Jose Manuel Alvarez; Antonio Lopez | ||||
Title | Photometric Invariance by Machine Learning | Type | Book Chapter | ||
Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
Volume | 7 | Issue | Pages | 113-134 | |
Keywords | road detection | ||||
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Publisher | iConcept Press Ltd | Place of Publication | Editor | Theo Gevers, Arjan Gijsenij, Joost van de Weijer, Jan-Mark Geusebroek | |
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ISSN | ISBN | 978-0-470-89084-4 | Medium | ||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ AlL2012 | Serial | 2186 | ||
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Author | Jose Manuel Alvarez | ||||
Title | On-Board Road Surface Segmentation | Type | Report | ||
Year | 2007 | Publication | CVC Technical Report #108 | Abbreviated Journal | |
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Address | CVC (UAB) | ||||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Alv2007 | Serial | 820 | ||
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Author | Jose Manuel Alvarez | ||||
Title | Combining Context and Appearance for Road Detection | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Road traffic crashes have become a major cause of death and injury throughout the world.
Hence, in order to improve road safety, the automobile manufacture is moving towards the development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene. In this way, we present two different approaches to infer the geometry of the road ahead the moving vehicle. Finally, we focus on combining these context and appearance (color) approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Theo Gevers | |
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ISSN | ISBN | 978-84-937261-8-8 | Medium | ||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Alv2010 | Serial | 1454 | ||
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Author | 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 | ||||
Title | Environmental Perception for Intelligent Vehicles | Type | Book Chapter | ||
Year | 2018 | Publication | Intelligent Vehicles. Enabling Technologies and Future Developments | Abbreviated Journal | |
Volume | Issue | Pages | 23–101 | ||
Keywords | Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles | ||||
Abstract | 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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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @AAA2018 | Serial | 3046 | ||
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Author | Jose Luis Gomez; Manuel Silva; Antonio Seoane; Agnes Borras; Mario Noriega; German Ros; Jose Antonio Iglesias; Antonio Lopez | ||||
Title | All for One, and One for All: UrbanSyn Dataset, the third Musketeer of Synthetic Driving Scenes | Type | Miscellaneous | ||
Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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Abstract | We introduce UrbanSyn, a photorealistic dataset acquired through semi-procedurally generated synthetic urban driving scenarios. Developed using high-quality geometry and materials, UrbanSyn provides pixel-level ground truth, including depth, semantic segmentation, and instance segmentation with object bounding boxes and occlusion degree. It complements GTAV and Synscapes datasets to form what we coin as the 'Three Musketeers'. We demonstrate the value of the Three Musketeers in unsupervised domain adaptation for image semantic segmentation. Results on real-world datasets, Cityscapes, Mapillary Vistas, and BDD100K, establish new benchmarks, largely attributed to UrbanSyn. We make UrbanSyn openly and freely accessible (this http URL). | ||||
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Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ GSS2023 | Serial | 4015 | ||
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Author | Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez | ||||
Title | Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches | Type | Journal Article | ||
Year | 2021 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 21 | Issue | 9 | Pages | 3185 |
Keywords | co-training; multi-modality; vision-based object detection; ADAS; self-driving | ||||
Abstract | Top-performing computer vision models are powered by convolutional neural networks (CNNs). Training an accurate CNN highly depends on both the raw sensor data and their associated ground truth (GT). Collecting such GT is usually done through human labeling, which is time-consuming and does not scale as we wish. This data-labeling bottleneck may be intensified due to domain shifts among image sensors, which could force per-sensor data labeling. In this paper, we focus on the use of co-training, a semi-supervised learning (SSL) method, for obtaining self-labeled object bounding boxes (BBs), i.e., the GT to train deep object detectors. In particular, we assess the goodness of multi-modal co-training by relying on two different views of an image, namely, appearance (RGB) and estimated depth (D). Moreover, we compare appearance-based single-modal co-training with multi-modal. Our results suggest that in a standard SSL setting (no domain shift, a few human-labeled data) and under virtual-to-real domain shift (many virtual-world labeled data, no human-labeled data) multi-modal co-training outperforms single-modal. In the latter case, by performing GAN-based domain translation both co-training modalities are on par, at least when using an off-the-shelf depth estimation model not specifically trained on the translated images. | ||||
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Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ GVL2021 | Serial | 3562 | ||
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Author | Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez | ||||
Title | Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models | Type | Journal Article | ||
Year | 2023 | Publication | Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” | Abbreviated Journal | SENS |
Volume | 23 | Issue | 2 | Pages | 621 |
Keywords | Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving | ||||
Abstract | Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic
segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our procedure shows improvements ranging from ∼13 to ∼26 mIoU points over baselines, so establishing new state-of-the-art results. |
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Notes | ADAS; no proj | Approved | no | ||
Call Number | Admin @ si @ GVL2023 | Serial | 3705 | ||
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