|
Records |
Links |
|
Author |
Bhaskar Chakraborty |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts |
Type |
Miscellaneous |
|
Year |
2008 |
Publication |
CVC Technical Report #123 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Barcelona, Spain |
|
|
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 |
|
|
|
Notes |
ISE |
Approved |
no |
|
|
Call Number |
Admin @ si @ Cha2008 |
Serial |
1149 |
|
Permanent link to this record |
|
|
|
|
Author |
Bhaskar Chakraborty; Ognjen Rudovic; Jordi Gonzalez |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component-Wise HMM of Body Parts |
Type |
Conference Article |
|
Year |
2008 |
Publication |
8th IEEE International Conference on Automatic Face and Gesture Recognition |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Amsterdam; The Netherlands |
|
|
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 |
FG |
|
|
Notes |
ISE |
Approved |
no |
|
|
Call Number |
ISE @ ise @ CRG2008 |
Serial |
1113 |
|
Permanent link to this record |
|
|
|
|
Author |
F. Javier Sanchez; Jordi Vitria |
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
ViLi + : Extended Lisp for image Processing and Computer Vision. |
Type |
Conference Article |
|
Year |
1994 |
Publication |
Progress in Image Analysis and Processing III |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
World Scientific |
Place of Publication |
|
Editor |
S.Impedovo |
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
981-02-1552-5 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
MV;OR |
Approved |
no |
|
|
Call Number |
BCNPCL @ bcnpcl @ SaV1994; IAM @ iam @ SaV1994 |
Serial |
114 |
|
Permanent link to this record |
|
|
|
|
Author |
David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Virtual and Real World Adaptation for Pedestrian Detection |
Type |
Journal Article |
|
Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume |
36 |
Issue |
4 |
Pages |
797-809 |
|
|
Keywords |
Domain Adaptation; Pedestrian Detection |
|
|
Abstract |
Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. |
|
|
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 |
0162-8828 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.057; 600.054; 600.076 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ VML2014 |
Serial |
2275 |
|
Permanent link to this record |
|
|
|
|
Author |
Javier Marin |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Virtual learning for real testing |
Type |
Report |
|
Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
|
|
Volume |
150 |
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
|
|
Publisher |
|
Place of Publication |
bell |
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ Mar2009c |
Serial |
2403 |
|
Permanent link to this record |
|
|
|
|
Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules |
Type |
Conference Article |
|
Year |
2023 |
Publication |
37th International Congress and Exhibition is organized by Computer Assisted Radiology and Surgery |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Pòster |
|
|
Address |
Munich; Germany; June 2023 |
|
|
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 |
CARS |
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ TGR2023a |
Serial |
3950 |
|
Permanent link to this record |
|
|
|
|
Author |
Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project |
Type |
Journal Article |
|
Year |
2023 |
Publication |
International Journal of Computer Assisted Radiology and Surgery |
Abbreviated Journal |
IJCARS |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
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 |
|
|
|
Notes |
IAM |
Approved |
no |
|
|
Call Number |
Admin @ si @ TGM2023 |
Serial |
3830 |
|
Permanent link to this record |
|
|
|
|
Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; Javier Marin |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Virtual Worlds and Active Learning for Human Detection |
Type |
Conference Article |
|
Year |
2011 |
Publication |
13th International Conference on Multimodal Interaction |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
393-400 |
|
|
Keywords |
Pedestrian Detection; Human detection; Virtual; Domain Adaptation; Active Learning |
|
|
Abstract |
Image based human detection is of paramount interest due to its potential applications in fields such as advanced driving assistance, surveillance and media analysis. However, even detecting non-occluded standing humans remains a challenge of intensive research. The most promising human detectors rely on classifiers developed in the discriminative paradigm, i.e., trained with labelled samples. However, labeling is a manual intensive step, especially in cases like human detection where it is necessary to provide at least bounding boxes framing the humans for training. To overcome such problem, some authors have proposed the use of a virtual world where the labels of the different objects are obtained automatically. This means that the human models (classifiers) are learnt using the appearance of rendered images, i.e., using realistic computer graphics. Later, these models are used for human detection in images of the real world. The results of this technique are surprisingly good. However, these are not always as good as the classical approach of training and testing with data coming from the same camera, or similar ones. Accordingly, in this paper we address the challenge of using a virtual world for gathering (while playing a videogame) a large amount of automatically labelled samples (virtual humans and background) and then training a classifier that performs equal, in real-world images, than the one obtained by equally training from manually labelled real-world samples. For doing that, we cast the problem as one of domain adaptation. In doing so, we assume that a small amount of manually labelled samples from real-world images is required. To collect these labelled samples we propose a non-standard active learning technique. Therefore, ultimately our human model is learnt by the combination of virtual and real world labelled samples (Fig. 1), which has not been done before. We present quantitative results showing that this approach is valid. |
|
|
Address |
Alicante, Spain |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
ACM DL |
Place of Publication |
New York, NY, USA, USA |
Editor |
|
|
|
Language |
English |
Summary Language |
English |
Original Title |
Virtual Worlds and Active Learning for Human Detection |
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-1-4503-0641-6 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ICMI |
|
|
Notes |
ADAS |
Approved |
yes |
|
|
Call Number |
ADAS @ adas @ VLP2011a |
Serial |
1683 |
|
Permanent link to this record |
|
|
|
|
Author |
J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Visible-Thermal Fusion based Monocular Visual Odometry |
Type |
Conference Article |
|
Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
Abbreviated Journal |
|
|
|
Volume |
417 |
Issue |
|
Pages |
517-528 |
|
|
Keywords |
Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion. |
|
|
Abstract |
The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
|
|
Address |
Lisboa; Portugal; November 2015 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer International Publishing |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2194-5357 |
ISBN |
978-3-319-27145-3 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ROBOT |
|
|
Notes |
ADAS; 600.076; 600.086 |
Approved |
no |
|
|
Call Number |
Admin @ si @ PAD2015 |
Serial |
2663 |
|
Permanent link to this record |
|
|
|
|
Author |
Wenlong Deng; Yongli Mou; Takahiro Kashiwa; Sergio Escalera; Kohei Nagai; Kotaro Nakayama; Yutaka Matsuo; Helmut Prendinger |
![goto web page url](img/www.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Vision based Pixel-level Bridge Structural Damage Detection Using a Link ASPP Network |
Type |
Journal Article |
|
Year |
2020 |
Publication |
Automation in Construction |
Abbreviated Journal |
AC |
|
|
Volume |
110 |
Issue |
|
Pages |
102973 |
|
|
Keywords |
Semantic image segmentation; Deep learning |
|
|
Abstract |
Structural Health Monitoring (SHM) has greatly benefited from computer vision. Recently, deep learning approaches are widely used to accurately estimate the state of deterioration of infrastructure. In this work, we focus on the problem of bridge surface structural damage detection, such as delamination and rebar exposure. It is well known that the quality of a deep learning model is highly dependent on the quality of the training dataset. Bridge damage detection, our application domain, has the following main challenges: (i) labeling the damages requires knowledgeable civil engineering professionals, which makes it difficult to collect a large annotated dataset; (ii) the damage area could be very small, whereas the background area is large, which creates an unbalanced training environment; (iii) due to the difficulty to exactly determine the extension of the damage, there is often a variation among different labelers who perform pixel-wise labeling. In this paper, we propose a novel model for bridge structural damage detection to address the first two challenges. This paper follows the idea of an atrous spatial pyramid pooling (ASPP) module that is designed as a novel network for bridge damage detection. Further, we introduce the weight balanced Intersection over Union (IoU) loss function to achieve accurate segmentation on a highly unbalanced small dataset. The experimental results show that (i) the IoU loss function improves the overall performance of damage detection, as compared to cross entropy loss or focal loss, and (ii) the proposed model has a better ability to detect a minority class than other light segmentation networks. |
|
|
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 |
|
|
|
Notes |
HuPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ DMK2020 |
Serial |
3314 |
|
Permanent link to this record |
|
|
|
|
Author |
David Geronimo; David Vazquez; Arturo de la Escalera |
![goto web page url](img/www.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
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 |
|
|
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 |
|
|
|
Notes |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ GVE2017 |
Serial |
2881 |
|
Permanent link to this record |
|
|
|
|
Author |
Sebastian Ramos |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Vision-based Detection of Road Hazards for Autonomous Driving |
Type |
Report |
|
Year |
2014 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
UAB; September 2014 |
|
|
Corporate Author |
|
Thesis |
Master's 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 |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @ Ram2014 |
Serial |
2580 |
|
Permanent link to this record |
|
|
|
|
Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
Type |
Conference Article |
|
Year |
2015 |
Publication |
IEEE Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
231 - 238 |
|
|
Keywords |
Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
|
|
Abstract |
Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
|
|
Address |
Hawaii; January 2015 |
|
|
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 |
ACDC |
Expedition |
|
Conference |
WACV |
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ RRG2015 |
Serial |
2499 |
|
Permanent link to this record |
|
|
|
|
Author |
David Geronimo; Antonio Lopez |
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Vision-based Pedestrian Protection Systems for Intelligent Vehicles |
Type |
Book Whole |
|
Year |
2014 |
Publication |
SpringerBriefs in Computer Science |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1-114 |
|
|
Keywords |
Computer Vision; Driver Assistance Systems; Intelligent Vehicles; Pedestrian Detection; Vulnerable Road Users |
|
|
Abstract |
Pedestrian Protection Systems (PPSs) are on-board systems aimed at detecting and tracking people in the surroundings of a vehicle in order to avoid potentially dangerous situations. These systems, together with other Advanced Driver Assistance Systems (ADAS) such as lane departure warning or adaptive cruise control, are one of the most promising ways to improve traffic safety. By the use of computer vision, cameras working either in the visible or infra-red spectra have been demonstrated as a reliable sensor to perform this task. Nevertheless, the variability of human’s appearance, not only in terms of clothing and sizes but also as a result of their dynamic shape, makes pedestrians one of the most complex classes even for computer vision. Moreover, the unstructured changing and unpredictable environment in which such on-board systems must work makes detection a difficult task to be carried out with the demanded robustness. In this brief, the state of the art in PPSs is introduced through the review of the most relevant papers of the last decade. A common computational architecture is presented as a framework to organize each method according to its main contribution. More than 300 papers are referenced, most of them addressing pedestrian detection and others corresponding to the descriptors (features), pedestrian models, and learning machines used. In addition, an overview of topics such as real-time aspects, systems benchmarking and future challenges of this research area are presented. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Briefs in Computer Vision |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
978-1-4614-7986-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
ADAS; 600.076 |
Approved |
no |
|
|
Call Number |
GeL2014 |
Serial |
2325 |
|
Permanent link to this record |
|
|
|
|
Author |
Ferran Diego; Jose Manuel Alvarez; Joan Serrat; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Vision-based road detection via on-line video registration |
Type |
Conference Article |
|
Year |
2010 |
Publication |
13th Annual International Conference on Intelligent Transportation Systems |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1135–1140 |
|
|
Keywords |
video alignment; road detection |
|
|
Abstract |
TB6.2
Road segmentation is an essential functionality for supporting advanced driver assistance systems (ADAS) such as road following and vehicle and pedestrian detection. Significant efforts have been made in order to solve this task using vision-based techniques. The major challenge is to deal with lighting variations and the presence of objects on the road surface. In this paper, we propose a new road detection method to infer the areas of the image depicting road surfaces without performing any image segmentation. The idea is to previously segment manually or semi-automatically the road region in a traffic-free reference video record on a first drive. And then to transfer these regions to the frames of a second video sequence acquired later in a second drive through the same road, in an on-line manner. This is possible because we are able to automatically align the two videos in time and space, that is, to synchronize them and warp each frame of the first video to its corresponding frame in the second one. The geometric transform can thus transfer the road region to the present frame on-line. In order to reduce the different lighting conditions which are present in outdoor scenarios, our approach incorporates a shadowless feature space which represents an image in an illuminant-invariant feature space. Furthermore, we propose a dynamic background subtraction algorithm which removes the regions containing vehicles in the observed frames which are within the transferred road region. |
|
|
Address |
Madeira Island (Portugal) |
|
|
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 |
2153-0009 |
ISBN |
978-1-4244-7657-2 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
ITSC |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ DAS2010 |
Serial |
1424 |
|
Permanent link to this record |