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Author |
Angel Sappa; M.A. Garcia |
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Title |
Hierarchical Clustering of 3D Objects and its Application to Minimum Distance Computation |
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2004 |
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IEEE International Conference on Robotics & Automation, 5287–5292, New Orleans, LA (USA), ISBN: 0–7803–8232–3 |
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New Orleans, LA, USA |
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ADAS @ adas @ SaG2004b |
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459 |
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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 |
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5th Iberian Conference on Pattern Recognition and Image Analysis |
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6669 |
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363-370 |
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Pedestrian Detection; Color; Part Based Models |
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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 |
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Berlin Heidelberg |
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J. Vitria; J.M. Sanches; M. Hernandez |
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English |
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English |
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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 @ adas @ RVL2011a |
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1666 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Factorization with Missing and Noisy Data |
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2006 |
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6th International Conference on Computational Science |
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ICCS´06 |
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LNCS 3991 |
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555–562 |
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Reading (United Kingdom) |
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ADAS @ adas @ JSL2006b |
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653 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
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Title |
Real Time Vehicle Pose Using On-Board Stereo Vision System |
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Year |
2006 |
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International Conference on Image Analysis and Recognition |
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ICIAR |
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LNCS 4142 |
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205–216 |
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This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time,
relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented. |
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ADAS @ adas @ SGD2006b |
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671 |
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Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
An Iterative Multiresolution Scheme for SFM |
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Year |
2006 |
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International Conference on Image Analysis and Recognition |
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ICIAR 2006 |
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LNCS 4141 |
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1 |
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804–815 |
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ADAS @ adas @ JSL2006c |
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704 |
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Author |
Angel Sappa; Boris X. Vintimilla |
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Title |
Edge Point Linking by Means of Global and Local Schemes |
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2006 |
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IEEE Int. Conf. on Signal-Image Technology and Internet-Based Systems, Hammamet, Tunisia, December 2006, pp. 551-560. |
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Hammamet (Tunisia) |
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ADAS @ adas @ SaV2006 |
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722 |
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Author |
German Ros; J. Guerrero; Angel Sappa; Daniel Ponsa; Antonio Lopez |
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Title |
Fast and Robust l1-averaging-based Pose Estimation for Driving Scenarios |
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2013 |
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24th British Machine Vision Conference |
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SLAM |
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Robust visual pose estimation is at the core of many computer vision applications, being fundamental for Visual SLAM and Visual Odometry problems. During the last decades, many approaches have been proposed to solve these problems, being RANSAC one of the most accepted and used. However, with the arrival of new challenges, such as large driving scenarios for autonomous vehicles, along with the improvements in the data gathering frameworks, new issues must be considered. One of these issues is the capability of a technique to deal with very large amounts of data while meeting the realtime
constraint. With this purpose in mind, we present a novel technique for the problem of robust camera-pose estimation that is more suitable for dealing with large amount of data, which additionally, helps improving the results. The method is based on a combination of a very fast coarse-evaluation function and a robust ℓ1-averaging procedure. Such scheme leads to high-quality results while taking considerably less time than RANSAC.
Experimental results on the challenging KITTI Vision Benchmark Suite are provided, showing the validity of the proposed approach. |
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Bristol; UK; September 2013 |
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BMVC |
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Admin @ si @ RGS2013b; ADAS @ adas @ |
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2274 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Title |
Vehicle Trajectory Estimation based on Monocular Vision |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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587-594 |
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vehicle detection |
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Girona (Spain) |
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ADAS @ adas @ PoL2007a |
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785 |
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David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa |
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Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection |
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2007 |
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Proceedings of the 5th International Conference on Computer Vision Systems |
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ICVS |
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Pedestrian Detection |
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On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera
pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one. |
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Bielefeld (Germany) |
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ADAS @ adas @ gsl2007a |
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786 |
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Author |
Daniel Ponsa; Antonio Lopez |
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Feature Selection Based on a New Formulation of the Minimal-Redundancy-Maximal-Relevance Criterion |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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47-54 |
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Girona (Spain) |
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ADAS @ adas @ PoL2007b |
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787 |
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