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Daniel Marczak; Grzegorz Rypesc; Sebastian Cygert; Tomasz Trzcinski; Bartłomiej Twardowski |
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Title |
Generalized Continual Category Discovery |
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Miscellaneous |
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2023 |
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arxiv |
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Most of Continual Learning (CL) methods push the limit of supervised learning settings, where an agent is expected to learn new labeled tasks and not forget previous knowledge. However, these settings are not well aligned with real-life scenarios, where a learning agent has access to a vast amount of unlabeled data encompassing both novel (entirely unlabeled) classes and examples from known classes. Drawing inspiration from Generalized Category Discovery (GCD), we introduce a novel framework that relaxes this assumption. Precisely, in any task, we allow for the existence of novel and known classes, and one must use continual version of unsupervised learning methods to discover them. We call this setting Generalized Continual Category Discovery (GCCD). It unifies CL and GCD, bridging the gap between synthetic benchmarks and real-life scenarios. With a series of experiments, we present that existing methods fail to accumulate knowledge from subsequent tasks in which unlabeled samples of novel classes are present. In light of these limitations, we propose a method that incorporates both supervised and unsupervised signals and mitigates the forgetting through the use of centroid adaptation. Our method surpasses strong CL methods adopted for GCD techniques and presents a superior representation learning performance. |
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LAMP |
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Admin @ si @ MRC2023 |
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3985 |
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Author |
Daniel Hernandez; Lukas Schneider; P. Cebrian; A. Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan Carlos Moure |
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Title |
Slanted Stixels: A way to represent steep streets |
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Journal Article |
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Year |
2019 |
Publication |
International Journal of Computer Vision |
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IJCV |
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127 |
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1643–1658 |
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This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
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ADAS; 600.118; 600.124 |
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Admin @ si @ HSC2019 |
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3304 |
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Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure |
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Title |
Slanted Stixels: Representing San Francisco's Steepest Streets} |
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Conference Article |
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Year |
2017 |
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28th British Machine Vision Conference |
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In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset. |
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London; uk; September 2017 |
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BMVC |
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ADAS; 600.118 |
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ADAS @ adas @ HSE2017a |
Serial |
2945 |
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Author |
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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GTC |
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ADAS; 600.085; 600.082; 600.076 |
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Call Number |
ADAS @ adas @ HME2016 |
Serial |
2738 |
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Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure |
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Title |
GPU-accelerated real-time stixel computation |
Type |
Conference Article |
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Year |
2017 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
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Pages |
1054-1062 |
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Keywords |
Autonomous Driving; GPU; Stixel |
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The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels. The goal of this work is to implement and evaluate a complete multi-stixel estimation pipeline on an embedded, energyefficient, GPU-accelerated device. This work presents a full GPU-accelerated implementation of stixel estimation that produces reliable results at 26 frames per second (real-time) on the Tegra X1 for disparity images of 1024×440 pixels and stixel widths of 5 pixels, and achieves more than 400 frames per second on a high-end Titan X GPU card. |
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Santa Rosa; CA; USA; March 2017 |
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WACV |
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ADAS; 600.118 |
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ADAS @ adas @ HEV2017b |
Serial |
2812 |
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Author |
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 |
Publication |
GPU Technology Conference |
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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 |
Serial |
2879 |
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Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure |
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Title |
3D Perception With Slanted Stixels on GPU |
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Journal Article |
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Year |
2021 |
Publication |
IEEE Transactions on Parallel and Distributed Systems |
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TPDS |
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Volume |
32 |
Issue |
10 |
Pages |
2434-2447 |
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Keywords |
Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure |
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Abstract |
This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier. |
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Notes |
ADAS; 600.124; 600.118 |
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Call Number |
Admin @ si @ HEV2021 |
Serial |
3561 |
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Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
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Title |
Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
Type |
Conference Article |
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Year |
2016 |
Publication |
16th International Conference on Computational Science |
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80 |
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143-153 |
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Keywords |
Autonomous Driving; Stereo; CUDA; 3d reconstruction |
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Abstract |
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
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San Diego; CA; USA; June 2016 |
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ICCS |
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ADAS; 600.085; 600.082; 600.076 |
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no |
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Call Number |
ADAS @ adas @ HCE2016a |
Serial |
2740 |
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Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
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Title |
Stereo Matching using SGM on the GPU |
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Report |
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Year |
2016 |
Publication |
Programming and Tuning Massively Parallel Systems |
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PUMPS |
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Keywords |
CUDA; Stereo; Autonomous Vehicle |
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Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy efficient GPU devices. Our design runs on a Tegra X1 at 42 frames per second (fps) for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method. |
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PUMPS |
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ADAS; 600.085; 600.087; 600.076 |
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ADAS @ adas @ HCE2016b |
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2776 |
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Author |
Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva |
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Title |
On Tracking Inside Groups |
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Journal Article |
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Year |
2010 |
Publication |
Machine Vision and Applications |
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MVA |
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21 |
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2 |
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113–127 |
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This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions. |
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Springer-Verlag |
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0932-8092 |
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ISE |
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ISE @ ise @ RGP2010 |
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1158 |
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Dani Rowe; Jordi Gonzalez; Ivan Huerta; Juan J. Villanueva |
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Title |
On Reasoning over Tracking Events |
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Conference Article |
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2007 |
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15th Scandinavian Conference on Image Analysis |
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4522 |
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502–511 |
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Aalborg (Denmark) |
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LNCS |
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SCIA´07 |
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ISE @ ise @ RGH2007 |
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784 |
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Dani Rowe; Ivan Huerta; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Robust Multiple-People Tracking Using Colour-Based Particle Filters |
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Book Chapter |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:113–120 |
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Girona (Spain) |
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ISE @ ise @ RHG2007 |
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782 |
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Author |
Dani Rowe; Ignasi Rius; Jordi Gonzalez; Xavier Roca; Juan J. Villanueva |
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Title |
Probabilistic Image-Based Tracking: Improving Particle Filtering |
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2005 |
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Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 85–92 |
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Estoril (Portugal) |
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ISE |
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ISE @ ise @ RRG2005a |
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545 |
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Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva |
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Robust Particle Filtering for Object Tracking |
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Miscellaneous |
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2005 |
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13th International Conference on Image Analysis and Processing (ICIAP’2005), LNCS 3617: 1158–1165, ISBN 3–540–28869–4 |
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Cagliary (Italy) |
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ISE @ ise @ RRG2005e |
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577 |
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| Permanent link to this record |
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Author |
Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Improving Tracking by Handling Occlusions |
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Miscellaneous |
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2005 |
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3rd International Conference on Advances in Pattern Recognition (ICAPR’2005), Pattern Recognition and Image Analysis, LNCS 3687: 146–154, ISBN 978–3–540–28833–6 |
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Bath (United Kingdom) |
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ISE @ ise @ RRG2005d |
Serial |
619 |
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