| |
Records |
Links |
|
Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
|
| |
Title |
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 |
Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez |
|
| |
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 |
IV |
|
| |
Notes |
ADAS; 600.076; 600.057; 600.054 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ GVX2015 |
Serial |
2625 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez |
|
| |
Title |
3D-Guided Multiscale Sliding Window for Pedestrian Detection |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
|
|
| |
Volume |
9117 |
Issue |
|
Pages |
560-568 |
|
| |
Keywords |
Pedestrian Detection |
|
| |
Abstract |
The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy. |
|
| |
Address |
Santiago de Compostela; España; 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 |
IbPRIA |
|
| |
Notes |
ADAS; 600.076; 600.057; 600.054 |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ GVR2015 |
Serial |
2585 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Hanne Kause; Patricia Marquez; Andrea Fuster; Aura Hernandez-Sabate; Luc Florack; Debora Gil; Hans van Assen |
|
| |
Title |
Quality Assessment of Optical Flow in Tagging MRI |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
5th Dutch Bio-Medical Engineering Conference BME2015 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
The Netherlands; 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 |
|
Expedition |
|
Conference |
BME |
|
| |
Notes |
IAM; ADAS; 600.076; 600.075 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KMF2015 |
Serial |
2616 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
|
| |
Title |
Cross-spectral image registration and fusion: an evaluation study |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
2nd International Conference on Machine Vision and Machine Learning |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
multispectral imaging; image registration; data fusion; infrared and visible spectra |
|
| |
Abstract |
This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
|
| |
Address |
Barcelona; July 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 |
|
Expedition |
|
Conference |
MVML |
|
| |
Notes |
ADAS; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CAV2015 |
Serial |
2629 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo |
|
| |
Title |
LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
22th IEEE International Conference on Image Processing |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
178 - 181 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
Quebec; Canada; September 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 |
|
Expedition |
|
Conference |
ICIP |
|
| |
Notes |
ADAS; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ AST2015 |
Serial |
2630 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Dennis G.Romero; Anselmo Frizera; Angel Sappa; Boris X. Vintimilla; Teodiano F.Bastos |
|
| |
Title |
A predictive model for human activity recognition by observing actions and context |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015 |
Abbreviated Journal |
|
|
| |
Volume |
9386 |
Issue |
|
Pages |
323-333 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach. |
|
| |
Address |
Catania; Italy; October 2015 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer International Publishing |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0302-9743 |
ISBN |
978-3-319-25902-4 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
ACIVS |
|
| |
Notes |
ADAS; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RFS2015 |
Serial |
2661 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias |
|
| |
Title |
Scene Representations for Autonomous Driving: an approach based on polygonal primitives |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
2nd Iberian Robotics Conference ROBOT2015 |
Abbreviated Journal |
|
|
| |
Volume |
417 |
Issue |
|
Pages |
503-515 |
|
| |
Keywords |
Scene reconstruction; Point cloud; Autonomous vehicles |
|
| |
Abstract |
In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
|
| |
Address |
Lisboa; Portugal; November 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 |
|
Expedition |
|
Conference |
ROBOT |
|
| |
Notes |
ADAS; 600.076; 600.086 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ OSS2015a |
Serial |
2662 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
|
| |
Title |
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 |
Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom |
|
| |
Title |
Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
International Conference on Intelligent Robots and Systems |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2488 - 2495 |
|
| |
Keywords |
Visual Learning; Computer Vision; Autonomous Agents |
|
| |
Abstract |
In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. |
|
| |
Address |
Hamburg; Germany; October 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 |
|
Expedition |
|
Conference |
IROS |
|
| |
Notes |
ADAS; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ OSL2015 |
Serial |
2664 |
|
| Permanent link to this record |
