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Dive into the research topics where German Ros is active.

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Featured researches published by German Ros.


computer vision and pattern recognition | 2016

The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

German Ros; Laura Sellart; Joanna Materzynska; David Vázquez; Antonio M. López

Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images, thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this paper, we propose to use a virtual world to automatically generate realistic synthetic images with pixel-level annotations. Then, we address the question of how useful such data can be for semantic segmentation - in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show how the inclusion of SYNTHIA in the training stage significantly improves performance on the semantic segmentation task.


workshop on applications of computer vision | 2015

Vision-Based Offline-Online Perception Paradigm for Autonomous Driving

German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vázquez; Antonio M. López

Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in real-time. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.


ieee intelligent vehicles symposium | 2015

Unsupervised image transformation for outdoor semantic labelling

German Ros; Jose M. Alvarez

Semantic labelling of urban images is a crucial component towards autonomous driving. The accuracy of current methods is highly dependent on the training set being used and drops drastically when the distribution in the test image does not match the expected distribution of the training set. This situation will inevitably occur, as for instance, when the illumination changes from daytime to dusk. To address this problem we propose a fast unsupervised image transformation approach following a global color transfer strategy. Our proposal generalizes classical one-to-one color transfer schemes to the more suitable one-to-many scheme. In addition, our approach can naturally deal with the temporal consistency of video streams to perform a coherent transformation. We demonstrate the benefits of our proposal in two publicly available datasets using different state-of-the-art semantic labelling frameworks.


arXiv: Computer Vision and Pattern Recognition | 2017

From Virtual to Real World Visual Perception Using Domain Adaptation—The DPM as Example

Antonio M. López; Jiaolong Xu; Jose Luis Gomez; David Vázquez; German Ros

Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing visual perception challenges, such as localizing certain object classes within an image, the learning of the involved classifiers turns out to be a practical bottleneck. The reason is that, at least, we have to frame object examples with bounding boxes in thousands of images. A priori, the more complex the model is regarding its number of parameters, the more annotated examples are required. This annotation task is performed by human oracles, which ends up in inaccuracies and errors in the annotations (aka ground truth) since the task is inherently very cumbersome and sometimes ambiguous. As an alternative, we have pioneered the use of virtual worlds for collecting such annotations automatically and with high precision. However, since the models learned with virtual data must operate in the real world, we still need to perform domain adaptation (DA). In this chapter, we revisit the DA of a Deformable Part-Based Model (DPM) as an exemplifying case of virtual- to real-world DA. As a use case, we address the challenge of vehicle detection for driver assistance, using different publicly available virtual-world data. While doing so, we investigate questions such as how does the domain gap behave due to virtual-vs-real data with respect to dominant object appearance per domain, as well as the role of photo-realism in the virtual world .


international conference on robotics and automation | 2013

VSLAM pose initialization via Lie groups and Lie algebras optimization

German Ros; Julio Guerrero; Angel Domingo Sappa; Daniel Ponsa; Antonio M. López

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.


arXiv: Computer Vision and Pattern Recognition | 2016

Motion Estimation via Robust Decomposition With Constrained Rank

German Ros; Jose M. Alvarez; Julio Guerrero

In this work, we address the problem of outlier detection for robust motion estimation by using modern sparse-low-rank decompositions, i.e., Robust PCA-like methods, to impose global rank constraints. Robust decompositions have shown to be good at splitting a corrupted matrix into an uncorrupted low-rank matrix and a sparse matrix, containing outliers. However, this process only works when matrices have relatively low rank with respect to their ambient space, a property not met in motion estimation problems. As a solution, we propose to exploit the partial information present in the decomposition to decide which matches are outliers. We provide evidences showing that even when it is not possible to recover an uncorrupted low-rank matrix, the resulting information can be exploited for outlier detection. To this end we propose the Robust Decomposition with Constrained Rank (RD-CR), a proximal gradient based method that enforces the rank constraints inherent to motion estimation. We also present a general framework to perform robust estimation for stereo Visual Odometry, based on our RD-CR and a simple but effective compressed optimization method that achieves high performance. Our evaluation on synthetic data and on the KITTI dataset demonstrates the applicability of our approach in complex scenarios and it yields state-of-the-art performance.


iberian conference on pattern recognition and image analysis | 2015

3D-Guided Multiscale Sliding Window for Pedestrian Detection

Alejandro González; Gabriel Villalonga; German Ros; David Vázquez; Antonio M. López

The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multi-modal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.


british machine vision conference | 2013

Fast and Robust

German Ros; Angel Domingo Sappa; Daniel Ponsa; Antonio M. López; Julio Guerrero

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.


Domain Adaptation in Computer Vision Applications | 2017

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German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Pérez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vázquez; Antonio M. López

Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (CNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, CNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labor which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learned to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation—in particular, when using a CNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with CNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation.


Image and Vision Computing | 2017

-averaging-based Pose Estimation for Driving Scenarios.

Antonio M. López; Gabriel Villalonga; Laura Sellart; German Ros; David Vázquez; Jiaolong Xu; Javier Marín; Azadeh Sadat Mozafari

Abstract Computer vision technologies are at the core of different advanced driver assistance systems (ADAS) and will play a key role in oncoming autonomous vehicles too. One of the main challenges for such technologies is to perceive the driving environment, i.e. to detect and track relevant driving information in a reliable manner (e.g. pedestrians in the vehicle route, free space to drive through). Nowadays it is clear that machine learning techniques are essential for developing such a visual perception for driving. In particular, the standard working pipeline consists of collecting data (i.e. on-board images), manually annotating the data (e.g. drawing bounding boxes around pedestrians), learning a discriminative data representation taking advantage of such annotations (e.g. a deformable part-based model, a deep convolutional neural network), and then assessing the reliability of such representation with the acquired data. In the last two decades most of the research efforts focused on representation learning (first, designing descriptors and learning classifiers; later doing it end-to-end). Hence, collecting data and, especially, annotating it, is essential for learning good representations. While this has been the case from the very beginning, only after the disruptive appearance of deep convolutional neural networks that it became a serious issue due to their data hungry nature. In this context, the problem is that manual data annotation is a tiresome work prone to errors. Accordingly, in the late 00’s we initiated a research line consisting of training visual models using photo-realistic computer graphics, especially focusing on assisted and autonomous driving. In this paper, we summarize such a work and show how it has become a new tendency with increasing acceptance.

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Antonio M. López

Autonomous University of Barcelona

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David Vázquez

Autonomous University of Barcelona

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Gabriel Villalonga

Autonomous University of Barcelona

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Laura Sellart

Autonomous University of Barcelona

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Daniel Ponsa

Autonomous University of Barcelona

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Jiaolong Xu

Autonomous University of Barcelona

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Jose M. Alvarez

Commonwealth Scientific and Industrial Research Organisation

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Angel Domingo Sappa

Escuela Superior Politecnica del Litoral

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Simon Stent

University of Cambridge

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