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Alejandro Gonzalez Alzate; Zhijie Fang; Yainuvis Socarras; Joan Serrat; David Vazquez; Jiaolong Xu; Antonio Lopez |
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Title |
Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison |
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Journal Article |
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Year |
2016 |
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Sensors |
Abbreviated Journal |
SENS |
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Volume |
16 |
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6 |
Pages |
820 |
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Keywords |
Pedestrian Detection; FIR |
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Despite all the significant advances in pedestrian detection brought by computer vision for driving assistance, it is still a challenging problem. One reason is the extremely varying lighting conditions under which such a detector should operate, namely day and night time. Recent research has shown that the combination of visible and non-visible imaging modalities may increase detection accuracy, where the infrared spectrum plays a critical role. The goal of this paper is to assess the accuracy gain of different pedestrian models (holistic, part-based, patch-based) when training with images in the far infrared spectrum. Specifically, we want to compare detection accuracy on test images recorded at day and nighttime if trained (and tested) using (a) plain color images, (b) just infrared images and (c) both of them. In order to obtain results for the last item we propose an early fusion approach to combine features from both modalities. We base the evaluation on a new dataset we have built for this purpose as well as on the publicly available KAIST multispectral dataset. |
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1424-8220 |
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ADAS; 600.085; 600.076; 600.082; 601.281 |
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ADAS @ adas @ GFS2016 |
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2754 |
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Author |
Zhijie Fang; David Vazquez; Antonio Lopez |
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Title |
On-Board Detection of Pedestrian Intentions |
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Journal Article |
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Year |
2017 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
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17 |
Issue |
10 |
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2193 |
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Keywords |
pedestrian intention; ADAS; self-driving |
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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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ADAS; 600.085; 600.076; 601.223; 600.116; 600.118 |
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Admin @ si @ FVL2017 |
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2983 |
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Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville |
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Title |
PixelVAE: A Latent Variable Model for Natural Images |
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2017 |
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5th International Conference on Learning Representations |
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Deep Learning; Unsupervised Learning |
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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. |
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Toulon; France; April 2017 |
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ICLR |
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ADAS; 600.085; 600.076; 601.281; 600.118 |
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ADAS @ adas @ GKA2017 |
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2815 |
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Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
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Title |
Hierarchical Adaptive Structural SVM for Domain Adaptation |
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Year |
2016 |
Publication |
International Journal of Computer Vision |
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IJCV |
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119 |
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2 |
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159-178 |
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Domain Adaptation; Pedestrian Detection |
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A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition. |
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Springer US |
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0920-5691 |
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ADAS; 600.085; 600.082; 600.076 |
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Admin @ si @ XRV2016 |
Serial |
2669 |
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Author |
Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
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Title |
Hierarchical online domain adaptation of deformable part-based models |
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Conference Article |
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2016 |
Publication |
IEEE International Conference on Robotics and Automation |
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5536-5541 |
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Domain Adaptation; Pedestrian Detection |
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We propose an online domain adaptation method for the deformable part-based model (DPM). The online domain adaptation is based on a two-level hierarchical adaptation tree, which consists of instance detectors in the leaf nodes and a category detector at the root node. Moreover, combined with a multiple object tracking procedure (MOT), our proposal neither requires target-domain annotated data nor revisiting the source-domain data for performing the source-to-target domain adaptation of the DPM. From a practical point of view this means that, given a source-domain DPM and new video for training on a new domain without object annotations, our procedure outputs a new DPM adapted to the domain represented by the video. As proof-of-concept we apply our proposal to the challenging task of pedestrian detection. In this case, each instance detector is an exemplar classifier trained online with only one pedestrian per frame. The pedestrian instances are collected by MOT and the hierarchical model is constructed dynamically according to the pedestrian trajectories. Our experimental results show that the adapted detector achieves the accuracy of recent supervised domain adaptation methods (i.e., requiring manually annotated targetdomain data), and improves the source detector more than 10 percentage points. |
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Stockholm; Sweden; May 2016 |
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ICRA |
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Notes |
ADAS; 600.085; 600.082; 600.076 |
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Call Number |
Admin @ si @ XVM2016 |
Serial |
2728 |
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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 |
Publication |
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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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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Year |
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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ADAS @ adas @ HME2016 |
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2738 |
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German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez |
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Title |
The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes |
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Conference Article |
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Year |
2016 |
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29th IEEE Conference on Computer Vision and Pattern Recognition |
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3234-3243 |
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Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task |
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Las Vegas; USA; June 2016 |
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CVPR |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ RSM2016 |
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2739 |
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Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
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Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
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Conference Article |
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2016 |
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16th International Conference on Computational Science |
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80 |
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143-153 |
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Autonomous Driving; Stereo; CUDA; 3d reconstruction |
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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 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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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ HCE2016a |
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2740 |
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Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez |
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GPU-based pedestrian detection for autonomous driving |
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Conference Article |
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2016 |
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16th International Conference on Computational Science |
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80 |
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2377-2381 |
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Pedestrian detection; Autonomous Driving; CUDA |
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We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study. |
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San Diego; CA; USA; June 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ CSE2016 |
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2741 |
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Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez |
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Comparison of two non-linear model-based control strategies for autonomous vehicles |
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2016 |
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24th Mediterranean Conference on Control and Automation |
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846-851 |
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Autonomous Driving; Control |
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This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation. |
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Athens; Greece; June 2016 |
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MED |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ ASP2016 |
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2750 |
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Author |
Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts |
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Journal Article |
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2017 |
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IEEE Transactions on cybernetics |
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Cyber |
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47 |
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11 |
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3980 - 3990 |
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Multicue; multimodal; multiview; object detection |
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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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2168-2267 |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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Admin @ si @ |
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2810 |
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| Permanent link to this record |
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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 |
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Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
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Book Chapter |
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2017 |
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Domain Adaptation in Computer Vision Applications |
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12 |
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227-241 |
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SYNTHIA; Virtual worlds; Autonomous Driving |
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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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Springer |
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Gabriela Csurka |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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ADAS @ adas @ RSV2017 |
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2882 |
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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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2016 |
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Programming and Tuning Massively Parallel Systems |
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PUMPS |
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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 |
Serial |
2776 |
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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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Year |
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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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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Admin @ si @ DRC2017 |
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2988 |
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| Permanent link to this record |
