|
Records |
Links |
|
Author |
Henry Velesaca; Steven Araujo; Patricia Suarez; Angel Sanchez; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Off-the-Shelf Based System for Urban Environment Video Analytics |
Type |
Conference Article |
|
Year |
2020 |
Publication |
27th International Conference on Systems, Signals and Image Processing |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
greenhouse gases; carbon footprint; object detection; object tracking; website framework; off-the-shelf video analytics |
|
|
Abstract |
This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to
public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach. |
|
|
Address |
Virtual IWSSIP |
|
|
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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IWSSIP |
|
|
Notes |
MSIAU; 600.130; 601.349; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ VAS2020 |
Serial |
3429 |
|
Permanent link to this record |
|
|
|
|
Author |
Lluis Gomez; Dimosthenis Karatzas |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Scene Text Recognition: No Country for Old Men? |
Type |
Conference Article |
|
Year |
2014 |
Publication |
1st International Workshop on Robust Reading |
Abbreviated Journal |
|
|
|
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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IWRR |
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GoK2014c |
Serial |
2538 |
|
Permanent link to this record |
|
|
|
|
Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Automatic Image-Based Waste Classification |
Type |
Conference Article |
|
Year |
2019 |
Publication |
International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning |
Abbreviated Journal |
|
|
|
Volume |
11487 |
Issue |
|
Pages |
422–431 |
|
|
Keywords |
Computer Vision; Deep learning; Convolutional neural networks; Waste classification |
|
|
Abstract |
The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset. |
|
|
Address |
Almeria; June 2019 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IWINAC |
|
|
Notes |
LAMP; 600.120 |
Approved |
no |
|
|
Call Number |
RSV2019 |
Serial |
3273 |
|
Permanent link to this record |
|
|
|
|
Author |
Fadi Dornaika; Bogdan Raducanu |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Efficient Facial Expression Recognition for Human Robot Interaction |
Type |
Conference Article |
|
Year |
2007 |
Publication |
Computational and Ambient Intelligence, 9th International Work–Conference on Artificial Neural Networks |
Abbreviated Journal |
|
|
|
Volume |
4507 |
Issue |
|
Pages |
700–708 |
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IWANN |
|
|
Notes |
OR;MV |
Approved |
no |
|
|
Call Number |
BCNPCL @ bcnpcl @ DoR2007a |
Serial |
792 |
|
Permanent link to this record |
|
|
|
|
Author |
Xavier Perez Sala; Cecilio Angulo; Sergio Escalera |
![goto web page (via DOI) doi](img/doi.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Biologically Inspired Path Execution Using SURF Flow in Robot Navigation |
Type |
Conference Article |
|
Year |
2011 |
Publication |
11th International Work Conference on Artificial Neural Networks |
Abbreviated Journal |
|
|
|
Volume |
II |
Issue |
|
Pages |
581--588 |
|
|
Keywords |
|
|
|
Abstract |
An exportable and robust system using only camera images is proposed for path execution in robot navigation. Motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames, so the method is called SURF flow. This information is used to correct robot displacement when a straight forward path command is sent to the robot, but it is not really executed due to several robot and environmental concerns. The proposed system has been successfully tested on the legged robot Aibo. |
|
|
Address |
Malaga |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer Berlin Heidelberg |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0302-9743 |
ISBN |
978-3-642-21497-4 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IWANN |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ PAE2011b |
Serial |
1773 |
|
Permanent link to this record |
|
|
|
|
Author |
Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas |
![goto web page url](img/www.gif)
|
|
Title |
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets |
Type |
Conference Article |
|
Year |
2018 |
Publication |
International Workshop on Artificial Intelligence and Pattern Recognition |
Abbreviated Journal |
|
|
|
Volume |
11047 |
Issue |
|
Pages |
34-42 |
|
|
Keywords |
Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis |
|
|
Abstract |
The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall. |
|
|
Address |
Cuba; September 2018 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IWAIPR |
|
|
Notes |
MILAB; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ BGÑ2018 |
Serial |
3237 |
|
Permanent link to this record |
|
|
|
|
Author |
German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Visual SLAM for Driverless Cars: A Brief Survey |
Type |
Conference Article |
|
Year |
2012 |
Publication |
IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
SLAM |
|
|
Abstract |
|
|
|
Address |
Alcalá de Henares |
|
|
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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IVW |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ RSP2012; ADAS @ adas |
Serial |
2019 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Manuel Alvarez; Felipe Lumbreras; Theo Gevers; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Geographic Information for vision-based Road Detection |
Type |
Conference Article |
|
Year |
2010 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
621–626 |
|
|
Keywords |
road detection |
|
|
Abstract |
Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features, easy integration, low cost or low power consumption. Common vision-based road detection approaches use low-level features (such as color or texture) as visual cues to group pixels exhibiting similar properties. However, it is difficult to foresee a perfect clustering algorithm since roads are in outdoor scenarios being imaged from a mobile platform. In this paper, we propose a novel high-level approach to vision-based road detection based on geographical information. The key idea of the algorithm is exploiting geographical information to provide a rough detection of the road. Then, this segmentation is refined at low-level using color information to provide the final result. The results presented show the validity of our approach. |
|
|
Address |
San Diego; CA; USA |
|
|
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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS;ISE |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ ALG2010 |
Serial |
1428 |
|
Permanent link to this record |
|
|
|
|
Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title |
Pedestrian Candidates Generation using Monocular Cues |
Type |
Conference Article |
|
Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
7-12 |
|
|
Keywords |
pedestrian detection |
|
|
Abstract |
Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE Xplore |
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 |
978-1-4673-2119-8 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d |
Serial |
2013 |
|
Permanent link to this record |
|
|
|
|
Author |
Naveen Onkarappa; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title |
An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed |
Type |
Conference Article |
|
Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1138-1143 |
|
|
Keywords |
|
|
|
Abstract |
Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow. |
|
|
Address |
Alcalá de Henares |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE Xplore |
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 |
978-1-4673-2119-8 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ NaS2012 |
Serial |
2020 |
|
Permanent link to this record |
|
|
|
|
Author |
Miguel Oliveira; Angel Sappa; V. Santos |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
Title |
Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models |
Type |
Conference Article |
|
Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
299-303 |
|
|
Keywords |
|
|
|
Abstract |
The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches. |
|
|
Address |
Alcalá de Henares |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
IEEE Xplore |
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 |
978-1-4673-2119-8 |
Medium |
|
|
|
Area |
|
Expedition |
|
Conference ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS |
Approved |
no |
|
|
Call Number |
Admin @ si @ OSS2012b |
Serial |
2021 |
|
Permanent link to this record |
|
|
|
|
Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
|
|
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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS; 600.054; 600.057 |
Approved |
no |
|
|
Call Number |
XVL2013; ADAS @ adas @ xvl2013a |
Serial |
2214 |
|
Permanent link to this record |
|
|
|
|
Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection |
Type |
Conference Article |
|
Year |
2015 |
Publication |
IEEE Intelligent Vehicles Symposium IV2015 |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
356-361 |
|
|
Keywords |
Pedestrian Detection |
|
|
Abstract |
Despite recent significant advances, pedestrian 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 multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, 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. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. |
|
|
Address |
Seoul; Corea; June 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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS; 600.076; 600.057; 600.054 |
Approved |
no |
|
|
Call Number |
ADAS @ adas @ GVX2015 |
Serial |
2625 |
|
Permanent link to this record |
|
|
|
|
Author |
Zhijie Fang; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Is the Pedestrian going to Cross? Answering by 2D Pose Estimation |
Type |
Conference Article |
|
Year |
2018 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1271 - 1276 |
|
|
Keywords |
|
|
|
Abstract |
Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-ofthe-art results. |
|
|
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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS; 600.124; 600.116; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ FaL2018 |
Serial |
3181 |
|
Permanent link to this record |
|
|
|
|
Author |
Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Monocular Depth Estimation by Learning from Heterogeneous Datasets |
Type |
Conference Article |
|
Year |
2018 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
2176 - 2181 |
|
|
Keywords |
|
|
|
Abstract |
Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation. |
|
|
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 ![sorted by Conference field, descending order (down)](img/sort_desc.gif) |
IV |
|
|
Notes |
ADAS; 600.124; 600.116; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GUH2018 |
Serial |
3183 |
|
Permanent link to this record |