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Joan Serrat; Ferran Diego; Felipe Lumbreras |
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Los faros delanteros a traves del objetivo |
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2008 |
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UAB Divulga, Revista de divulgacion cientifica |
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ADAS |
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ADAS @ adas @ SDL2008b |
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1471 |
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Author |
Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira |
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Title |
Dynamic Comparison of Headlights |
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2008 |
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Journal of Automobile Engineering |
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222 |
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5 |
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643–656 |
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video alignment |
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ADAS @ adas @ SDL2008a |
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958 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; Daniel Ponsa |
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Title |
Multiple target tracking for intelligent headlights control |
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2012 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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13 |
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2 |
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594-605 |
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Intelligent Headlights |
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Intelligent vehicle lighting systems aim at automatically regulating the headlights' beam to illuminate as much of the road ahead as possible while avoiding dazzling other drivers. A key component of such a system is computer vision software that is able to distinguish blobs due to vehicles' headlights and rear lights from those due to road lamps and reflective elements such as poles and traffic signs. In a previous work, we have devised a set of specialized supervised classifiers to make such decisions based on blob features related to its intensity and shape. Despite the overall good performance, there remain challenging that have yet to be solved: notably, faint and tiny blobs corresponding to quite distant vehicles. In fact, for such distant blobs, classification decisions can be taken after observing them during a few frames. Hence, incorporating tracking could improve the overall lighting system performance by enforcing the temporal consistency of the classifier decision. Accordingly, this paper focuses on the problem of constructing blob tracks, which is actually one of multiple-target tracking (MTT), but under two special conditions: We have to deal with frequent occlusions, as well as blob splits and merges. We approach it in a novel way by formulating the problem as a maximum a posteriori inference on a Markov random field. The qualitative (in video form) and quantitative evaluation of our new MTT method shows good tracking results. In addition, we will also see that the classification performance of the problematic blobs improves due to the proposed MTT algorithm. |
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Admin @ si @ RLP2012; ADAS @ adas @ rsl2012g |
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1877 |
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Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez |
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Title |
Co-Training for Deep Object Detection: Comparing Single-Modal and Multi-Modal Approaches |
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2021 |
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Sensors |
Abbreviated Journal |
SENS |
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21 |
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9 |
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3185 |
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co-training; multi-modality; vision-based object detection; ADAS; self-driving |
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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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ADAS; 600.118 |
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Admin @ si @ GVL2021 |
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3562 |
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Author |
Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez |
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Title |
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models |
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2023 |
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Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” |
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SENS |
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23 |
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2 |
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621 |
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Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving |
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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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ADAS; no proj |
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no |
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Admin @ si @ GVL2023 |
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3705 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Title |
Road Detection Based on Illuminant Invariance |
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2011 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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12 |
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1 |
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184-193 |
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road detection |
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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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ADAS @ adas @ AlL2011 |
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1456 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras |
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Title |
Combining Priors, Appearance and Context for Road Detection |
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2014 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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15 |
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3 |
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1168-1178 |
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Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout |
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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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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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1524-9050 |
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ADAS; 600.076;ISE |
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Admin @ si @ ALG2014 |
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2501 |
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Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning photometric invariance for object detection |
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2010 |
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International Journal of Computer Vision |
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IJCV |
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90 |
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1 |
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45-61 |
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road detection |
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Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods |
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Springer US |
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0920-5691 |
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ADAS;ISE |
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ADAS @ adas @ AGL2010c |
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1451 |
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Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez |
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Road Geometry Classification by Adaptative Shape Models |
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2013 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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14 |
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1 |
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459-468 |
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road detection |
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Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions. |
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ADAS;ISE |
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Admin @ si @ AGD2013;; ADAS @ adas @ |
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2269 |
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Author |
Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez |
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A reduced feature set for driver head pose estimation |
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2016 |
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Applied Soft Computing |
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ASOC |
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45 |
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98-107 |
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Head pose estimation; driving performance evaluation; subspace based methods; linear regression |
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Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. |
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ADAS; 600.085; 600.076; |
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Admin @ si @ DHL2016 |
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2760 |
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