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| Author | Cesar de Souza; Adrien Gaidon; Yohann Cabon; Antonio Lopez | ||||
| Title | Procedural Generation of Videos to Train Deep Action Recognition Networks | Type | Conference Article | ||
| Year | 2017 | Publication | 30th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2594-2604 | ||
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| Abstract | Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for ”Procedural Human Action Videos”. It contains a total of 39, 982 videos, with more than 1, 000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly
outperforming fine-tuning state-of-the-art unsupervised generative models of videos. |
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| Address | Honolulu; Hawaii; July 2017 | ||||
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| Area | Expedition | Conference | CVPR | ||
Notes  |
ADAS; 600.076; 600.085; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SGC2017 | Serial | 3051 | ||
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| Author | Zhijie Fang; David Vazquez; Antonio Lopez | ||||
| Title | On-Board Detection of Pedestrian Intentions | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 10 | Pages | 2193 |
| Keywords | pedestrian intention; ADAS; self-driving | ||||
| Abstract | Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes |
ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ FVL2017 | Serial | 2983 | ||
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| Author | Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville | ||||
| Title | PixelVAE: A Latent Variable Model for Natural Images | Type | Conference Article | ||
| Year | 2017 | Publication | 5th International Conference on Learning Representations | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Deep Learning; Unsupervised Learning | ||||
| Abstract | Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms. | ||||
| Address | Toulon; France; April 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICLR | ||
| Notes |
ADAS; 600.085; 600.076; 601.281; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ GKA2017 | Serial | 2815 | ||
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| Author | Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores | ||||
| Title | On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | Cyber |
| Volume | 47 | Issue | 11 | Pages | 3980 - 3990 |
| Keywords | Multicue; multimodal; multiview; object detection | ||||
| Abstract | Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 2168-2267 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes |
ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2810 | ||
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| Author | German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez | ||||
| Title | Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA | Type | Book Chapter | ||
| Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
| Volume | 12 | Issue | Pages | 227-241 | |
| Keywords | SYNTHIA; Virtual worlds; Autonomous Driving | ||||
| Abstract | 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 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 learnt 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 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 and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. | ||||
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| Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
| Language | Summary Language | Original Title | |||
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| Notes |
ADAS; 600.085; 600.082; 600.076; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ RSV2017 | Serial | 2882 | ||
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| Author | Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun | ||||
| Title | CARLA: An Open Urban Driving Simulator | Type | Conference Article | ||
| Year | 2017 | Publication | 1st Annual Conference on Robot Learning. Proceedings of Machine Learning | Abbreviated Journal | |
| Volume | 78 | Issue | Pages | 1-16 | |
| Keywords | Autonomous driving; sensorimotor control; simulation | ||||
| 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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| Address | Mountain View; CA; USA; November 2017 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CORL | ||
| Notes |
ADAS; 600.085; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DRC2017 | Serial | 2988 | ||
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| Author | Antonio Lopez; Jiaolong Xu; Jose Luis Gomez; David Vazquez; German Ros | ||||
| Title | From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example | Type | Book Chapter | ||
| Year | 2017 | Publication | Domain Adaptation in Computer Vision Applications | Abbreviated Journal | |
| Volume | Issue | 13 | Pages | 243-258 | |
| Keywords | Domain Adaptation | ||||
| Abstract | 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. | ||||
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| Publisher | Springer | Place of Publication | Editor | Gabriela Csurka | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| Notes |
ADAS; 600.085; 601.223; 600.076; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ LXG2017 | Serial | 2872 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 4 | Pages | 873 |
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| Abstract | This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. | ||||
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| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ASA2017 | Serial | 2914 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Cross-Spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach. |
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| Address | San Sebastian; Spain; May 2017 | ||||
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| Area | Expedition | Conference | ECMSM | ||
| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017a | Serial | 2916 | ||
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| Author | Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa | ||||
| Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
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| Abstract | In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point that minimizes the vacuum force needed to guarantee a grasp. Experimental results in real scenarios are presented to show the validity of the proposed approach. |
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| Address | San Sebastian; Spain; May 2017 | ||||
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| Area | Expedition | Conference | ECMSM | ||
| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ VIS2017 | Serial | 2917 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Infrared Image Colorization based on a Triplet DCGAN Architecture | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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| Abstract | This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time. |
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| Address | Honolulu; Hawaii; USA; July 2017 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes |
ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017b | Serial | 2920 | ||
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| Author | Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure | ||||
| Title | GPU-accelerated real-time stixel computation | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1054-1062 | ||
| Keywords | Autonomous Driving; GPU; Stixel | ||||
| Abstract | 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. | ||||
| Address | Santa Rosa; CA; USA; March 2017 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes |
ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ HEV2017b | Serial | 2812 | ||
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| Author | Daniel Hernandez; Lukas Schneider; Antonio Espinosa; David Vazquez; Antonio Lopez; Uwe Franke; Marc Pollefeys; Juan C. Moure | ||||
| Title | Slanted Stixels: Representing San Francisco's Steepest Streets} | Type | Conference Article | ||
| Year | 2017 | Publication | 28th British Machine Vision Conference | Abbreviated Journal | |
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| Abstract | 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. | ||||
| Address | London; uk; September 2017 | ||||
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| Area | Expedition | Conference | BMVC | ||
| Notes |
ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ HSE2017a | Serial | 2945 | ||
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| Author | Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure | ||||
| Title | Embedded Real-time Stixel Computation | Type | Conference Article | ||
| Year | 2017 | Publication | GPU Technology Conference | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | GPU; CUDA; Stixels; Autonomous Driving | ||||
| Abstract | |||||
| Address | Silicon Valley; USA; May 2017 | ||||
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| Area | Expedition | Conference | GTC | ||
| Notes |
ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ HEV2017a | Serial | 2879 | ||
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| Author | David Geronimo; David Vazquez; Arturo de la Escalera | ||||
| Title | Vision-Based Advanced Driver Assistance Systems | Type | Book Chapter | ||
| Year | 2017 | Publication | Computer Vision in Vehicle Technology: Land, Sea, and Air | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | ADAS; Autonomous Driving | ||||
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ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ GVE2017 | Serial | 2881 | ||
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