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Author |
Xavier Baro; Jordi Gonzalez; Junior Fabian; Miguel Angel Bautista; Marc Oliu; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera |
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Title |
ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition |
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Conference Article |
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Year |
2015 |
Publication |
2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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1-9 |
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Abstract |
Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. |
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Boston; EEUU; June 2015 |
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CVPRW |
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HuPBA;MV |
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no |
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2652 |
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Author |
Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Automatic Ground-truthing using video registration for on-board detection algorithms |
Type |
Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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Pages |
4389 - 4392 |
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Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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ADAS |
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no |
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ADAS @ adas @ ADS2009 |
Serial |
1201 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
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Conference Article |
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Year |
2009 |
Publication |
22nd IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
565–572 |
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Keywords |
road detection |
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Abstract |
Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively 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, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may 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 taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. |
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Miami (USA) |
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1063-6919 |
ISBN |
978-1-4244-3992-8 |
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CVPR |
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ADAS;ISE |
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no |
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Call Number |
ADAS @ adas @ AGL2009 |
Serial |
1169 |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
3D Scene Priors for Road Detection |
Type |
Conference Article |
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Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
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57–64 |
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Keywords |
road detection |
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Abstract |
Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods. |
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San Francisco; CA; USA; June 2010 |
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1063-6919 |
ISBN |
978-1-4244-6984-0 |
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CVPR |
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ADAS;ISE |
Approved |
no |
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Call Number |
ADAS @ adas @ AGL2010a |
Serial |
1302 |
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Author |
Jaume Amores; David Geronimo; Antonio Lopez |
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Title |
Multiple instance and active learning for weakly-supervised object-class segmentation |
Type |
Conference Article |
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Year |
2010 |
Publication |
3rd IEEE International Conference on Machine Vision |
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Multiple Instance Learning; Active Learning; Object-class segmentation. |
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Abstract |
In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse
segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy.
As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set. |
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Hong-Kong |
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ICMV |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ AGL2010b |
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1429 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
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Title |
Shadow Resistant Road Segmentation from a Mobile Monocular System |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 |
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road detection |
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Address |
Gerona (Spain) |
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Notes |
ADAS;CIC |
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no |
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Call Number |
ADAS @ adas @ ALB2007 |
Serial |
943 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
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Title |
Illuminant Invariant Model-Based Road Segmentation |
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Conference Article |
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Year |
2008 |
Publication |
IEEE Intelligent Vehicles Symposium, |
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1155–1180 |
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road detection |
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Eindhoven (The Netherlands) |
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Notes |
ADAS;CIC |
Approved |
no |
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Call Number |
ADAS @ adas @ ALB2008 |
Serial |
1045 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez |
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Title |
Geographic Information for vision-based Road Detection |
Type |
Conference Article |
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Year |
2010 |
Publication |
IEEE Intelligent Vehicles Symposium |
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621–626 |
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road detection |
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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. |
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San Diego; CA; USA |
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IV |
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ADAS;ISE |
Approved |
no |
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Call Number |
ADAS @ adas @ ALG2010 |
Serial |
1428 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
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Title |
Novel Index for Objective Evaluation of Road Detection Algorithms |
Type |
Conference Article |
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Year |
2008 |
Publication |
Intelligent Transportation Systems. 11th International IEEE Conference on, |
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815–820 |
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road detection |
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Address |
Beijing (Xina) |
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ITSC |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ AlL2008 |
Serial |
1074 |
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Author |
Jaume Amores |
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Title |
Vocabulary-based Approaches for Multiple-Instance Data: a Comparative Study |
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Conference Article |
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Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
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4246–4250 |
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Multiple Instance Learning (MIL) has become a hot topic and many different algorithms have been proposed in the last years. Despite this fact, there is a lack of comparative studies that shed light into the characteristics of the different methods and their behavior in different scenarios. In this paper we provide such an analysis. We include methods from different families, and pay special attention to vocabulary-based approaches, a new family of methods that has not received much attention in the MIL literature. The empirical comparison includes seven databases from four heterogeneous domains, implementations of eight popular MIL methods, and a study of the behavior under synthetic conditions. Based on this analysis, we show that, with an appropriate implementation, vocabulary-based approaches outperform other MIL methods in most of the cases, showing in general a more consistent performance. |
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Istanbul, Turkey |
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1051-4651 |
ISBN |
978-1-4244-7542-1 |
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ICPR |
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ADAS |
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no |
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ADAS @ adas @ Amo2010 |
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1295 |
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Author |
Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez |
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Title |
Comparison of two non-linear model-based control strategies for autonomous vehicles |
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Conference Article |
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2016 |
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24th Mediterranean Conference on Control and Automation |
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846-851 |
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Autonomous Driving; Control |
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This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation. |
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Athens; Greece; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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no |
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ADAS @ adas @ ASP2016 |
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2750 |
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Author |
Jaume Amores; N. Sebe; Petia Radeva |
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Title |
Class-Specific Binaryy Correlograms for Object Recognition |
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Conference Article |
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2007 |
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British Machine Vision Conference |
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Warwick (UK) |
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BMVC’07 |
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ADAS;MILAB |
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no |
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ADAS @ adas @ ASR2007a |
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923 |
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Author |
Xavier Boix; Josep M. Gonfaus; Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Marco Pedersoli; Jordi Gonzalez; Joan Serrat |
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Title |
Combining local and global bag-of-word representations for semantic segmentation |
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Conference Article |
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2009 |
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Workshop on The PASCAL Visual Object Classes Challenge |
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Kyoto (Japan) |
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ICCV |
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ADAS;ISE |
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no |
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ADAS @ adas @ BGS2009 |
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1273 |
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Permanent link to this record |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space |
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Conference Article |
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2010 |
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17th IEEE International Conference on Image Processing |
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2749–2752 |
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This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach. |
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Hong-Kong |
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Edition |
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ISSN |
1522-4880 |
ISBN |
978-1-4244-7992-4 |
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ICIP |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ BLS2010 |
Serial |
1358 |
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Author |
Hugo Berti; Angel Sappa; Osvaldo Agamennoni |
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Title |
Autonomous robot navigation with a global and asymptotic convergence |
Type |
Conference Article |
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Year |
2007 |
Publication |
IEEE International Conference on Robotics and Automation |
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Volume |
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Pages |
2712–2717 |
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Abstract |
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Address |
Roma (Italy) |
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Series Editor |
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Edition |
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Conference |
ICRA |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ BSA2007 |
Serial |
796 |
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