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Author |
David Aldavert; Ricardo Toledo; Arnau Ramisa; Ramon Lopez de Mantaras |
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Title |
Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing |
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Conference Article |
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Year |
2009 |
Publication |
5th International Symposium on Visual Computing |
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Volume |
5875 |
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44–55 |
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Abstract |
In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost. |
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Las Vegas, USA |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-10330-8 |
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ISVC |
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ADAS |
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no |
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Call Number |
Admin @ si @ ATR2009a |
Serial |
1246 |
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Author |
Muhammad Anwer Rao; David Vazquez; Antonio Lopez |
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Title |
Opponent Colors for Human Detection |
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Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
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Pages |
363-370 |
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Keywords |
Pedestrian Detection; Color; Part Based Models |
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Abstract |
Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper. |
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Las Palmas de Gran Canaria. Spain |
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Springer |
Place of Publication |
Berlin Heidelberg |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
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Language |
English |
Summary Language |
English |
Original Title |
Opponent Colors for Human Detection |
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Lecture Notes on Computer Science |
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LNCS |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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ADAS |
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no |
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ADAS @ adas @ RVL2011a |
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1666 |
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Author |
Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Efficient On-Board Stereo Vision Pose Estimation |
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Conference Article |
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Year |
2007 |
Publication |
Computer Aided Systems Theory, Selected paper from |
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4739 |
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Pages |
1183–1190 |
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This paper presents an efficient technique for real time estimation of on-board stereo vision system pose. The whole process is performed in the Euclidean space and consists of two stages. Initially, a compact representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the 3D road points. Fast RANSAC fitting is obtained by selecting points according to a probability distribution function that takes into account the density of points at a given depth. Finally, stereo camera position
and orientation—pose—is computed relative to the road plane. The proposed technique is intended to be used on driver assistance systems for applications such as obstacle or pedestrian detection. A real time performance is reached. Experimental results on several environments and comparisons with a previous work are presented. |
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Las Palmas de Gran Canaria (Spain) |
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Conference |
EUROCAST |
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Notes |
ADAS |
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no |
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Call Number |
ADAS @ adas @ SDG2007b |
Serial |
916 |
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Author |
Angel Sappa; Rosa Herrero; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Road Approximation in Euclidean and v-Disparity Space: A Comparative Study |
Type |
Conference Article |
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Year |
2007 |
Publication |
Computer Aided Systems Theory, |
Abbreviated Journal |
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Volume |
4739 |
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Pages |
1105–1112 |
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Abstract |
This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge. |
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Las Palmas de Gran Canaria (Spain) |
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EUROCAST |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ SHD2007b |
Serial |
917 |
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Author |
Angel Sappa; Rosa Herrero; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Road Approximation in Euclidean and v-Disparity Space: A Comparative Study |
Type |
Conference Article |
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Year |
2007 |
Publication |
EUROCAST2007, Workshop on Cybercars and Intelligent Vehicles |
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Pages |
368–369 |
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Abstract |
This paper presents a comparative study between two road approximation techniques—planar surfaces—from stereo vision data. The first approach is carried out in the v-disparity space and is based on a voting scheme, the Hough transform. The second one consists in computing the best fitting plane for the whole 3D road data points, directly in the Euclidean space, by using least squares fitting. The comparative study is initially performed over a set of different synthetic surfaces
(e.g., plane, quadratic surface, cubic surface) digitized by a virtual stereo head; then real data obtained with a commercial stereo head are used. The comparative study is intended to be used as a criterion for fining the best technique according to the road geometry. Additionally, it highlights common problems driven from a wrong assumption about the scene’s prior knowledge. |
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Las Palmas de Gran Canaria (Spain) |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ SHD2007a |
Serial |
936 |
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Author |
Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez |
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Title |
Multi-task Bilinear Classifiers for Visual Domain Adaptation |
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Conference Article |
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Year |
2013 |
Publication |
Advances in Neural Information Processing Systems Workshop |
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Keywords |
Domain Adaptation; Pedestrian Detection; ADAS |
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We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.
The bilinear classifier encodes the feature transformation and classification
parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
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Lake Tahoe; Nevada; USA; December 2013 |
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NIPSW |
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Notes |
ADAS; 600.054; 600.057; 601.217;ISE |
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no |
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Call Number |
ADAS @ adas @ XRH2013 |
Serial |
2340 |
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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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Year |
2009 |
Publication |
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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ADAS @ adas @ BGS2009 |
Serial |
1273 |
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Author |
German Ros; J. Guerrero; Angel Sappa; Antonio Lopez |
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Title |
VSLAM pose initialization via Lie groups and Lie algebras optimization |
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Conference Article |
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Year |
2013 |
Publication |
Proceedings of IEEE International Conference on Robotics and Automation |
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5740 - 5747 |
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SLAM |
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We present a novel technique for estimating initial 3D poses in the context of localization and Visual SLAM problems. The presented approach can deal with noise, outliers and a large amount of input data and still performs in real time in a standard CPU. Our method produces solutions with an accuracy comparable to those produced by RANSAC but can be much faster when the percentage of outliers is high or for large amounts of input data. On the current work we propose to formulate the pose estimation as an optimization problem on Lie groups, considering their manifold structure as well as their associated Lie algebras. This allows us to perform a fast and simple optimization at the same time that conserve all the constraints imposed by the Lie group SE(3). Additionally, we present several key design concepts related with the cost function and its Jacobian; aspects that are critical for the good performance of the algorithm. |
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Karlsruhe; Germany; May 2013 |
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ISSN |
1050-4729 |
ISBN |
978-1-4673-5641-1 |
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Conference |
ICRA |
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Notes |
ADAS; 600.054; 600.055; 600.057 |
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no |
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Admin @ si @ RGS2013a; ADAS @ adas @ |
Serial |
2225 |
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Author |
Antonio Lopez; J. Hilgenstock; A. Busse; Ramon Baldrich; Felipe Lumbreras; Joan Serrat |
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Title |
Nightime Vehicle Detecion for Intelligent Headlight Control |
Type |
Conference Article |
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Year |
2008 |
Publication |
Advanced Concepts for Intelligent Vision Systems, 10th International Conference, Proceedings, |
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5259 |
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113–124 |
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Keywords |
Intelligent Headlights; vehicle detection |
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Address |
Juan-les-Pins, France |
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LNCS |
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ACIVS |
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ADAS;CIC |
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no |
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ADAS @ adas @ LHB2008a |
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1098 |
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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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Abstract |
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 |
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978-1-4244-7542-1 |
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ICPR |
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ADAS |
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ADAS @ adas @ Amo2010 |
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1295 |
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