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Author |
Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
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Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
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Conference Article |
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Year |
2016 |
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5th International Conference Games and Learning Alliance |
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10056 |
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50-59 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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GALA |
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ADAS;IAM; |
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HAC2016 |
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2864 |
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Author |
Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez |
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Title |
GPU-based pedestrian detection for autonomous driving |
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Conference Article |
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Year |
2016 |
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GPU Technology Conference |
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Pedestrian Detection; GPU |
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Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. |
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Silicon Valley; San Francisco; USA; April 2016 |
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GTC |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ CSM2016 |
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2737 |
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Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez |
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Title |
Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching |
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Conference Article |
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2016 |
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GPU Technology Conference |
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Stereo; Autonomous Driving; GPU; 3d reconstruction |
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Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms |
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Silicon Valley; San Francisco; USA; April 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ HME2016 |
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2738 |
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Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure |
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Title |
Embedded Real-time Stixel Computation |
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Conference Article |
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Year |
2017 |
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GPU Technology Conference |
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Keywords |
GPU; CUDA; Stixels; Autonomous Driving |
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Silicon Valley; USA; May 2017 |
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GTC |
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ADAS; 600.118 |
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ADAS @ adas @ HEV2017a |
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2879 |
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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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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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Keywords |
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 |
Editor |
J. Vitria; J.M. Sanches; M. Hernandez |
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English |
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English |
Original Title |
Opponent Colors for Human Detection |
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Lecture Notes on Computer Science |
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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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ADAS @ adas @ RVL2011a |
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1666 |
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Author |
Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez |
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Title |
Synchronization of Video Sequences from Free-moving Cameras |
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Conference Article |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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620–627 |
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Girona (Spain) |
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J. Marti et al. |
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IbPRIA |
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ADAS |
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ADAS @ adas @ SDL2007 |
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880 |
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Author |
Antonio Lopez; Joan Serrat; Cristina Cañero; Felipe Lumbreras |
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Title |
Robust Lane Lines Detection and Quantitative Assessment |
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Conference Article |
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2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis |
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4477 |
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274–281 |
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lane markings |
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Girona (Spain) |
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J. Marti et al |
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ADAS |
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ADAS @ adas @ LSC2007 |
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881 |
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Author |
Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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Conference Article |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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Keywords |
SSL; Pedestrian Detection |
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Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez |
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Title |
3D-Guided Multiscale Sliding Window for Pedestrian Detection |
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Conference Article |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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9117 |
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560-568 |
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Keywords |
Pedestrian Detection |
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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 multimodal (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. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.076; 600.057; 600.054 |
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ADAS @ adas @ GVR2015 |
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2585 |
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Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Title |
Single view facial hair 3D reconstruction |
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Conference Article |
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2019 |
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9th Iberian Conference on Pattern Recognition and Image Analysis |
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11867 |
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423-436 |
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3D Vision; Shape Reconstruction; Facial Hair Modeling |
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n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
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Madrid; July 2019 |
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IbPRIA |
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ADAS; 600.086; 600.130; 600.122 |
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Admin @ si @ |
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3707 |
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