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Author |
Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun |
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Title |
CARLA: An Open Urban Driving Simulator |
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Conference Article |
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2017 |
Publication |
1st Annual Conference on Robot Learning. Proceedings of Machine Learning |
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78 |
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1-16 |
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Autonomous driving; sensorimotor control; simulation |
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Abstract |
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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Mountain View; CA; USA; November 2017 |
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CORL |
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ADAS; 600.085; 600.118 |
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no |
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Call Number |
Admin @ si @ DRC2017 |
Serial |
2988 |
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Author |
Felipe Codevilla; Antonio Lopez; Vladlen Koltun; Alexey Dosovitskiy |
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Title |
On Offline Evaluation of Vision-based Driving Models |
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Conference Article |
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Year |
2018 |
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15th European Conference on Computer Vision |
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11219 |
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246-262 |
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Keywords |
Autonomous driving; deep learning |
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Autonomous driving models should ideally be evaluated by deploying
them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and
suitable offline metrics. |
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Munich; September 2018 |
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ECCV |
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ADAS; 600.124; 600.118 |
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Admin @ si @ CLK2018 |
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3162 |
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Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Towards Query-by-Speech Handwritten Keyword Spotting |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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501-505 |
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In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.084; 600.061; 601.223; 600.077;ADAS |
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Admin @ si @ RAT2015b |
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2682 |
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Author |
Fernando Barrera; Felipe Lumbreras; Cristhian Aguilera; Angel Sappa |
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Title |
Planar-Based Multispectral Stereo |
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2012 |
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11th Quantitative InfraRed Thermography |
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Naples, Italy |
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QIRT |
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ADAS |
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Admin @ si @ BLA2012 |
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2016 |
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Cristhian Aguilera; Fernando Barrera; Angel Sappa; Ricardo Toledo |
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Title |
A Novel SIFT-Like-Based Approach for FIR-VS Images Registration |
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2012 |
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11th Quantitative InfraRed Thermography |
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Naples, Italy |
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QIRT |
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ADAS; TV |
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Admin @ si @ ABS2012 |
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2017 |
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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 |
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ADAS @ adas @ SaG2004b |
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459 |
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Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Combining Holistic and Part-based Deep Representations for Computational Painting Categorization |
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2016 |
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6th International Conference on Multimedia Retrieval |
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Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification. |
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New York; USA; June 2016 |
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ICMR |
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LAMP; 600.068; 600.079;ADAS |
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Admin @ si @ RKW2016 |
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2763 |
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Author |
Marc Masana; Idoia Ruiz; Joan Serrat; Joost Van de Weijer; Antonio Lopez |
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Title |
Metric Learning for Novelty and Anomaly Detection |
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2018 |
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29th British Machine Vision Conference |
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When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works. |
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Newcastle; uk; September 2018 |
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BMVC |
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LAMP; ADAS; 601.305; 600.124; 600.106; 602.200; 600.120; 600.118 |
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Admin @ si @ MRS2018 |
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3156 |
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Author |
Angel Sappa; Fadi Dornaika; David Geronimo; Antonio Lopez |
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Title |
Registration-based Moving Object Detection from a Moving Camera |
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2008 |
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IROS2008 2nd Workshop on Perception, Planning and Navigation for Intelligent Vehicles |
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65–69 |
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This paper presents a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three stages. Initially, feature points are extracted and tracked through consecutive frames. Then, a RANSAC based approach is used for registering
two 3D point sets with known correspondences by means of the quaternion method. Finally, the computed 3D rigid displacement is used to map two consecutive frames into the same coordinate system. Moving objects correspond to those areas with large registration errors. Experimental results, in different scenarios, show the viability of the proposed approach. |
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Nice (France) |
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ADAS @ adas @ SDG2008 |
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1017 |
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Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Incremental Domain Adaptation of Deformable Part-based Models |
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2014 |
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25th British Machine Vision Conference |
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Pedestrian Detection; Part-based models; Domain Adaptation |
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Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers by adapting them from the previous training environment (source domain) to the new testing one (target domain)
is an approach with increasing acceptance in the computer vision community. In this paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data. |
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Nottingham; uk; September 2014 |
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BMVA Press |
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Valstar, Michel and French, Andrew and Pridmore, Tony |
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BMVC |
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ADAS; 600.057; 600.054; 600.076 |
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XRV2014c; ADAS @ adas @ xrv2014c |
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2455 |
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