| |
Records |
Links |
|
Author |
Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez |
|
| |
Title |
Embedded real-time stereo estimation via Semi-Global Matching on the GPU |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
|
|
| |
Volume |
80 |
Issue |
|
Pages |
143-153 |
|
| |
Keywords |
Autonomous Driving; Stereo; CUDA; 3d reconstruction |
|
| |
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. |
|
| |
Address |
San Diego; CA; USA; June 2016 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference  |
ICCS |
|
| |
Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ HCE2016a |
Serial |
2740 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez |
|
| |
Title |
GPU-based pedestrian detection for autonomous driving |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
16th International Conference on Computational Science |
Abbreviated Journal |
|
|
| |
Volume |
80 |
Issue |
|
Pages |
2377-2381 |
|
| |
Keywords |
Pedestrian detection; Autonomous Driving; CUDA |
|
| |
Abstract |
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. |
|
| |
Address |
San Diego; CA; USA; June 2016 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCS |
|
| |
Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ CSE2016 |
Serial |
2741 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Xavier Boix; Josep M. Gonfaus; Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Marco Pedersoli; Jordi Gonzalez; Joan Serrat |
|
| |
Title |
Combining local and global bag-of-word representations for semantic segmentation |
Type |
Conference Article |
| |
Year |
2009 |
Publication |
Workshop on The PASCAL Visual Object Classes Challenge |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Kyoto (Japan) |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCV |
|
| |
Notes |
ADAS;ISE |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ BGS2009 |
Serial |
1273 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mohammad Rouhani; Angel Sappa |
|
| |
Title |
Correspondence Free Registration through a Point-to-Model Distance Minimization |
Type |
Conference Article |
| |
Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2150-2157 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework. |
|
| |
Address |
Barcelona |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1550-5499 |
ISBN |
978-1-4577-1101-5 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCV |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RoS2011b; ADAS @ adas @ |
Serial |
1832 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe |
|
| |
Title |
Random Forests of Local Experts for Pedestrian Detection |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2592 - 2599 |
|
| |
Keywords |
ADAS; Random Forest; Pedestrian Detection |
|
| |
Abstract |
Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. |
|
| |
Address |
Sydney; Australia; December 2013 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1550-5499 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCV |
|
| |
Notes |
ADAS; 600.057; 600.054 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ MVL2013 |
Serial |
2333 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool |
|
| |
Title |
Active MAP Inference in CRFs for Efficient Semantic Segmentation |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2312 - 2319 |
|
| |
Keywords |
Semantic Segmentation |
|
| |
Abstract |
Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. |
|
| |
Address |
Sydney; Australia; December 2013 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1550-5499 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCV |
|
| |
Notes |
ADAS; 600.057 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ RBN2013 |
Serial |
2377 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Hamed H. Aghdam; Abel Gonzalez-Garcia; Joost Van de Weijer; Antonio Lopez |
|
| |
Title |
Active Learning for Deep Detection Neural Networks |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3672-3680 |
|
| |
Keywords |
|
|
| |
Abstract |
The cost of drawing object bounding boxes (ie labeling) for millions of images is prohibitively high. For instance, labeling pedestrians in a regular urban image could take 35 seconds on average. Active learning aims to reduce the cost of labeling by selecting only those images that are informative to improve the detection network accuracy. In this paper, we propose a method to perform active learning of object detectors based on convolutional neural networks. We propose a new image-level scoring process to rank unlabeled images for their automatic selection, which clearly outperforms classical scores. The proposed method can be applied to videos and sets of still images. In the former case, temporal selection rules can complement our scoring process. As a relevant use case, we extensively study the performance of our method on the task of pedestrian detection. Overall, the experiments show that the proposed method performs better than random selection. |
|
| |
Address |
Seul; Korea; October 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCV |
|
| |
Notes |
ADAS; LAMP; 600.124; 600.109; 600.141; 600.120; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ AGW2019 |
Serial |
3321 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Felipe Codevilla; Eder Santana; Antonio Lopez; Adrien Gaidon |
|
| |
Title |
Exploring the Limitations of Behavior Cloning for Autonomous Driving |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
18th IEEE International Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
9328-9337 |
|
| |
Keywords |
|
|
| |
Abstract |
Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to learn simple visuomotor policies end-to-end, but scaling to the full spectrum of driving behaviors remains an unsolved problem. In this paper, we propose a new benchmark to experimentally investigate the scalability and limitations of behavior cloning. We show that behavior cloning leads to state-of-the-art results, executing complex lateral and longitudinal maneuvers, even in unseen environments, without being explicitly programmed to do so. However, we confirm some limitations of the behavior cloning approach: some well-known limitations (eg, dataset bias and overfitting), new generalization issues (eg, dynamic objects and the lack of a causal modeling), and training instabilities, all requiring further research before behavior cloning can graduate to real-world driving. The code, dataset, benchmark, and agent studied in this paper can be found at github. |
|
| |
Address |
Seul; Korea; October 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCV |
|
| |
Notes |
ADAS; 600.124; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CSL2019 |
Serial |
3322 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate |
|
| |
Title |
A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth |
Type |
Conference Article |
| |
Year |
2011 |
Publication |
IEEE International Conference on Computer Vision – Workshops |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2042-2049 |
|
| |
Keywords |
IEEE International Conference on Computer Vision – Workshops |
|
| |
Abstract |
Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
IEEE |
Place of Publication |
Barcelona (Spain) |
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCVW |
|
| |
Notes |
IAM; ADAS |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ MGH2011 |
Serial |
1682 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Javad Zolfaghari Bengar; Abel Gonzalez-Garcia; Gabriel Villalonga; Bogdan Raducanu; Hamed H. Aghdam; Mikhail Mozerov; Antonio Lopez; Joost Van de Weijer |
|
| |
Title |
Temporal Coherence for Active Learning in Videos |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
IEEE International Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
914-923 |
|
| |
Keywords |
|
|
| |
Abstract |
Autonomous driving systems require huge amounts of data to train. Manual annotation of this data is time-consuming and prohibitively expensive since it involves human resources. Therefore, active learning emerged as an alternative to ease this effort and to make data annotation more manageable. In this paper, we introduce a novel active learning approach for object detection in videos by exploiting temporal coherence. Our active learning criterion is based on the estimated number of errors in terms of false positives and false negatives. The detections obtained by the object detector are used to define the nodes of a graph and tracked forward and backward to temporally link the nodes. Minimizing an energy function defined on this graphical model provides estimates of both false positives and false negatives. Additionally, we introduce a synthetic video dataset, called SYNTHIA-AL, specially designed to evaluate active learning for video object detection in road scenes. Finally, we show that our approach outperforms active learning baselines tested on two datasets. |
|
| |
Address |
Seul; Corea; October 2019 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICCVW |
|
| |
Notes |
LAMP; ADAS; 600.124; 602.200; 600.118; 600.120; 600.141 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ZGV2019 |
Serial |
3294 |
|
| Permanent link to this record |
