| |
Records |
Links |
|
Author  |
Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez |
|
| |
Title |
Cost-sensitive Structured SVM for Multi-category Domain Adaptation |
Type |
Conference Article |
| |
Year |
2014 |
Publication |
22nd International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
3886 - 3891 |
|
| |
Keywords |
Domain Adaptation; Pedestrian Detection |
|
| |
Abstract |
Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition. |
|
| |
Address |
Stockholm; Sweden; August 2014 |
|
| |
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 |
1051-4651 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ICPR |
|
| |
Notes |
ADAS; 600.057; 600.054; 601.217; 600.076 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ XRV2014a |
Serial |
2434 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez |
|
| |
Title |
Multi-task Bilinear Classifiers for Visual Domain Adaptation |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
Advances in Neural Information Processing Systems Workshop |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Domain Adaptation; Pedestrian Detection; ADAS |
|
| |
Abstract |
We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.
The bilinear classifier encodes the feature transformation and classification
parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
|
| |
Address |
Lake Tahoe; Nevada; USA; 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 |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
NIPSW |
|
| |
Notes |
ADAS; 600.054; 600.057; 601.217;ISE |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ XRH2013 |
Serial |
2340 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
|
| |
Title |
Incremental Domain Adaptation of Deformable Part-based Models |
Type |
Conference Article |
| |
Year |
2014 |
Publication |
25th British Machine Vision Conference |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Pedestrian Detection; Part-based models; Domain Adaptation |
|
| |
Abstract |
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. |
|
| |
Address |
Nottingham; uk; September 2014 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
BMVA Press |
Place of Publication |
|
Editor |
Valstar, Michel and French, Andrew and Pridmore, Tony |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
BMVC |
|
| |
Notes |
ADAS; 600.057; 600.054; 600.076 |
Approved |
no |
|
| |
Call Number |
XRV2014c; ADAS @ adas @ xrv2014c |
Serial |
2455 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
|
| |
Title |
DA-DPM Pedestrian Detection |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
ICCV Workshop on Reconstruction meets Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Domain Adaptation; Pedestrian Detection |
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
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-RR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ XRV2013 |
Serial |
2569 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; Peng Wang; Heng Yang; Antonio Lopez |
|
| |
Title |
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving |
Type |
Conference Article |
| |
Year |
2019 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2379-2384 |
|
| |
Keywords |
|
|
| |
Abstract |
Autonomous driving has harsh requirements of small model size and energy efficiency, in order to enable the embedded system to achieve real-time on-board object detection. Recent deep convolutional neural network based object detectors have achieved state-of-the-art accuracy. However, such models are trained with numerous parameters and their high computational costs and large storage prohibit the deployment to memory and computation resource limited systems. Low-precision neural networks are popular techniques for reducing the computation requirements and memory footprint. Among them, binary weight neural network (BWN) is the extreme case which quantizes the float-point into just bit. BWNs are difficult to train and suffer from accuracy deprecation due to the extreme low-bit representation. To address this problem, we propose a knowledge transfer (KT) method to aid the training of BWN using a full-precision teacher network. We built DarkNet-and MobileNet-based binary weight YOLO-v2 detectors and conduct experiments on KITTI benchmark for car, pedestrian and cyclist detection. The experimental results show that the proposed method maintains high detection accuracy while reducing the model size of DarkNet-YOLO from 257 MB to 8.8 MB and MobileNet-YOLO from 193 MB to 7.9 MB. |
|
| |
Address |
Montreal; Canada; May 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 |
ICRA |
|
| |
Notes |
ADAS; 600.124; 600.116; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ XWY2018 |
Serial |
3182 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa |
|
| |
Title |
Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
CVPR Workshop on Ground Truth – What is a good dataset? |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
688 - 693 |
|
| |
Keywords |
Pedestrian Detection; Domain Adaptation |
|
| |
Abstract |
Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%. |
|
| |
Address |
Portland; oregon; June 2013 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
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 |
CVPRW |
|
| |
Notes |
ADAS; 600.054; 600.057; 601.217 |
Approved |
yes |
|
| |
Call Number |
XVR2013; ADAS @ adas @ xvr2013a |
Serial |
2220 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; David Vazquez; Krystian Mikolajczyk; Antonio Lopez |
|
| |
Title |
Hierarchical online domain adaptation of deformable part-based models |
Type |
Conference Article |
| |
Year |
2016 |
Publication |
IEEE International Conference on Robotics and Automation |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
5536-5541 |
|
| |
Keywords |
Domain Adaptation; Pedestrian Detection |
|
| |
Abstract |
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. |
|
| |
Address |
Stockholm; Sweden; May 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 |
ICRA |
|
| |
Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ XVM2016 |
Serial |
2728 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
|
| |
Title |
Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection |
Type |
Conference Article |
| |
Year |
2013 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
467 - 472 |
|
| |
Keywords |
Pedestrian Detection; Virtual World; Part based |
|
| |
Abstract |
State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster). |
|
| |
Address |
Gold Coast; Australia; June 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 |
1931-0587 |
ISBN |
978-1-4673-2754-1 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
IV |
|
| |
Notes |
ADAS; 600.054; 600.057 |
Approved |
no |
|
| |
Call Number |
XVL2013; ADAS @ adas @ xvl2013a |
Serial |
2214 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez |
|
| |
Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
Type |
Conference Article |
| |
Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
137–144 |
|
| |
Keywords |
Pedestrian Detection; Domain Adaptation |
|
| |
Abstract |
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance. |
|
| |
Address |
San Francisco; CA; USA; June 2010 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
CVPR |
|
| |
Notes |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ MVG2010 |
Serial |
1304 |
|
| 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 |
