| Records |
| Author |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras; T. Graf |
| Title |
Robust lane markings detection and road geometry computation |
Type |
Journal Article |
| Year |
2010 |
Publication |
International Journal of Automotive Technology |
Abbreviated Journal |
IJAT |
| Volume |
11 |
Issue |
3 |
Pages |
395–407 |
| Keywords |
lane markings |
| Abstract |
Detection of lane markings based on a camera sensor can be a low-cost solution to lane departure and curve-over-speed warnings. A number of methods and implementations have been reported in the literature. However, reliable detection is still an issue because of cast shadows, worn and occluded markings, variable ambient lighting conditions, for example. We focus on increasing detection reliability in two ways. First, we employed an image feature other than the commonly used edges: ridges, which we claim addresses this problem better. Second, we adapted RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane lines to the image features, based on both ridgeness and ridge orientation. In addition, the model was fitted for the left and right lane lines simultaneously to enforce a consistent result. Four measures of interest for driver assistance applications were directly computed from the fitted parametric model at each frame: lane width, lane curvature, and vehicle yaw angle and lateral offset with regard the lane medial axis. We qualitatively assessed our method in video sequences captured on several road types and under very different lighting conditions. We also quantitatively assessed it on synthetic but realistic video sequences for which road geometry and vehicle trajectory ground truth are known. |
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The Korean Society of Automotive Engineers |
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Edition |
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| ISSN |
1229-9138 |
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| Notes |
ADAS |
Approved |
no |
Call Number  |
ADAS @ adas @ LSC2010 |
Serial |
1300 |
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| Author |
Felipe Lumbreras; Joan Serrat |
| Title |
Segmentation of petrographical images of marbles |
Type |
Journal Article |
| Year |
1996 |
Publication |
Computers and Geosciences. 22(5):547–558 |
Abbreviated Journal |
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ADAS |
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no |
| Call Number |
ADAS @ adas @ LuS1996b |
Serial |
82 |
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| Author |
Antonio Lopez; Ernest Valveny; Juan J. Villanueva |
| Title |
Real-time quality control of surgical material packaging by artificial vision |
Type |
Journal Article |
| Year |
2005 |
Publication |
Assembly Automation |
Abbreviated Journal |
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| Volume |
25 |
Issue |
3 |
Pages |
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| Keywords |
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| Abstract |
IF: 0.061) |
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| Notes |
ADAS;DAG |
Approved |
no |
| Call Number |
ADAS @ adas @ LVV2005 |
Serial |
552 |
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| Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva |
| Title |
Occlusion handling via random subspace classifiers for human detection |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Systems, Man, and Cybernetics (Part B) |
Abbreviated Journal |
TSMCB |
| Volume |
44 |
Issue |
3 |
Pages |
342-354 |
| Keywords |
Pedestriand Detection; occlusion handling |
| Abstract |
This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2168-2267 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ MVL2014 |
Serial |
2213 |
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| Author |
Daniel Ponsa; Antonio Lopez |
| Title |
Variance reduction techniques in particle-based visual contour Tracking |
Type |
Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
42 |
Issue |
11 |
Pages |
2372–2391 |
| Keywords |
Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling |
| Abstract |
This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done. |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ PoL2009a |
Serial |
1168 |
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| Author |
Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca |
| Title |
Quality control of safety belts by machine vision inspection for real-time production |
Type |
Journal Article |
| Year |
2003 |
Publication |
Optical Engineering (IF: 0.877) |
Abbreviated Journal |
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| Volume |
42 |
Issue |
4 |
Pages |
1114-1120 |
| Keywords |
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| Abstract |
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| Address |
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Thesis |
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| Publisher |
SPIE |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
ADAS;ISE;CIC |
Approved |
no |
| Call Number |
ADAS @ adas @ PRL2003 |
Serial |
399 |
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| |
| Author |
Daniel Ponsa; Joan Serrat; Antonio Lopez |
| Title |
On-board image-based vehicle detection and tracking |
Type |
Journal Article |
| Year |
2011 |
Publication |
Transactions of the Institute of Measurement and Control |
Abbreviated Journal |
TIM |
| Volume |
33 |
Issue |
7 |
Pages |
783-805 |
| Keywords |
vehicle detection |
| Abstract |
In this paper we present a computer vision system for daytime vehicle detection and localization, an essential step in the development of several types of advanced driver assistance systems. It has a reduced processing time and high accuracy thanks to the combination of vehicle detection with lane-markings estimation and temporal tracking of both vehicles and lane markings. Concerning vehicle detection, our main contribution is a frame scanning process that inspects images according to the geometry of image formation, and with an Adaboost-based detector that is robust to the variability in the different vehicle types (car, van, truck) and lighting conditions. In addition, we propose a new method to estimate the most likely three-dimensional locations of vehicles on the road ahead. With regards to the lane-markings estimation component, we have two main contributions. First, we employ a different image feature to the other commonly used edges: we use ridges, which are better suited to this problem. Second, we adapt RANSAC, a generic robust estimation method, to fit a parametric model of a pair of lane markings to the image features. We qualitatively assess our vehicle detection system in sequences captured on several road types and under very different lighting conditions. The processed videos are available on a web page associated with this paper. A quantitative evaluation of the system has shown quite accurate results (a low number of false positives and negatives) at a reasonable computation time. |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ PSL2011 |
Serial |
1413 |
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| Author |
A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva |
| Title |
Topological principal component analysis for face encoding and recognition |
Type |
Journal Article |
| Year |
2001 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
22 |
Issue |
6-7 |
Pages |
769–776 |
| Keywords |
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| Abstract |
IF: 0.552 |
| Address |
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Edition |
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| Notes |
ADAS;OR;MV |
Approved |
no |
| Call Number |
ADAS @ adas @ PVL2001 |
Serial |
155 |
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| Author |
Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira |
| Title |
Dynamic Comparison of Headlights |
Type |
Journal Article |
| Year |
2008 |
Publication |
Journal of Automobile Engineering |
Abbreviated Journal |
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| Volume |
222 |
Issue |
5 |
Pages |
643–656 |
| Keywords |
video alignment |
| Abstract |
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Place of Publication |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ SDL2008a |
Serial |
958 |
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| Author |
Angel Sappa; Fadi Dornaika; Daniel Ponsa; David Geronimo; Antonio Lopez |
| Title |
An Efficient Approach to Onboard Stereo Vision System Pose Estimation |
Type |
Journal Article |
| Year |
2008 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
9 |
Issue |
3 |
Pages |
476–490 |
| Keywords |
Camera extrinsic parameter estimation, ground plane estimation, onboard stereo vision system |
| Abstract |
This paper presents an efficient technique for estimating the pose of an onboard stereo vision system relative to the environment’s dominant surface area, which is supposed to be the road surface. Unlike previous approaches, it can be used either for urban or highway scenarios since it is not based on a specific visual traffic feature extraction but on 3-D raw data points. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact 2-D representation of the original 3-D data points is computed. Then, a RANdom SAmple Consensus (RANSAC) based least-squares approach is used to fit a plane to the road. Fast RANSAC fitting is obtained by selecting points according to a probability function that takes into account the density of points at a given depth. Finally, stereo camera height and pitch angle are computed related to the fitted road plane. The proposed technique is intended to be used in driverassistance systems for applications such as vehicle or pedestrian detection. Experimental results on urban environments, which are the most challenging scenarios (i.e., flat/uphill/downhill driving, speed bumps, and car’s accelerations), are presented. These results are validated with manually annotated ground truth. Additionally, comparisons with previous works are presented to show the improvements in the central processing unit processing time, as well as in the accuracy of the obtained results. |
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IEEE |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ SDP2008 |
Serial |
1000 |
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| Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
| Title |
On-board camera extrinsic parameter estimation |
Type |
Journal Article |
| Year |
2006 |
Publication |
Electronics Letters |
Abbreviated Journal |
EL |
| Volume |
42 |
Issue |
13 |
Pages |
745–746 |
| Keywords |
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| Abstract |
An efficient technique for real-time estimation of camera extrinsic parameters is presented. It is intended to be used on on-board vision systems for driving assistance applications. The proposed technique is based on the use of a commercial stereo vision system that does not need any visual feature extraction. |
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IEE |
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ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ SGD2006a |
Serial |
655 |
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| Author |
A.F. Sole; Antonio Lopez; G. Sapiro |
| Title |
Crease Enhancement Diffusion |
Type |
Journal Article |
| Year |
2001 |
Publication |
Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298) |
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| Address |
New York; USA |
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ADAS |
Approved |
no |
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ADAS @ adas @ SLS2001 |
Serial |
485 |
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| Author |
A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez |
| Title |
Anisotropic 2-D and 3-D Averaging of fMRI Signals |
Type |
Journal Article |
| Year |
2001 |
Publication |
IEEE Transactions on Medical Imaging, 20(2): 86–93 (IF: 3.142) |
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ADAS |
Approved |
no |
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ADAS @ adas @ SNS2001 |
Serial |
165 |
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| Author |
David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo |
| Title |
Virtual and Real World Adaptation for Pedestrian Detection |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
36 |
Issue |
4 |
Pages |
797-809 |
| Keywords |
Domain Adaptation; Pedestrian Detection |
| Abstract |
Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. |
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| ISSN |
0162-8828 |
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| Notes |
ADAS; 600.057; 600.054; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ VML2014 |
Serial |
2275 |
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| Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
| Title |
Domain Adaptation of Deformable Part-Based Models |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
36 |
Issue |
12 |
Pages |
2367-2380 |
| Keywords |
Domain Adaptation; Pedestrian Detection |
| Abstract |
The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors. |
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0162-8828 |
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| Notes |
ADAS; 600.057; 600.054; 601.217; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ XRV2014b |
Serial |
2436 |
| Permanent link to this record |
