| |
Records |
Links |
|
Author |
R. Valenti; Theo Gevers |
|
| |
Title  |
Combining Head Pose and Eye Location Information for Gaze Estimation |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
|
| |
Volume |
21 |
Issue |
2 |
Pages |
802-815 |
|
| |
Keywords |
|
|
| |
Abstract |
Impact factor 2010: 2.92
Impact factor 2011/12?: 3.32
Head pose and eye location for gaze estimation have been separately studied in numerous works in the literature. Previous research shows that satisfactory accuracy in head pose and eye location estimation can be achieved in constrained settings. However, in the presence of nonfrontal faces, eye locators are not adequate to accurately locate the center of the eyes. On the other hand, head pose estimation techniques are able to deal with these conditions; hence, they may be suited to enhance the accuracy of eye localization. Therefore, in this paper, a hybrid scheme is proposed to combine head pose and eye location information to obtain enhanced gaze estimation. To this end, the transformation matrix obtained from the head pose is used to normalize the eye regions, and in turn, the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to enhance the accuracy of eye location estimations, particularly in low-resolution videos, to extend the operative range of the eye locators, and to improve the accuracy of the head pose tracker. These enhanced estimations are then combined to obtain a novel visual gaze estimation system, which uses both eye location and head information to refine the gaze estimates. From the experimental results, it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Furthermore, it considerably extends its operating range by more than 15° by overcoming the problems introduced by extreme head poses. Moreover, the accuracy of the head pose tracker is improved by 12% to 24%. Finally, the experimentation on the proposed combined gaze estimation system shows that it is accurate (with a mean error between 2° and 5°) and that it can be used in cases where classic approaches would fail without imposing restraints on the position of the head. |
|
| |
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 |
1057-7149 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ALTRES;ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ VaG 2012b |
Serial |
1851 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Manuel Alvarez; Antonio Lopez; Theo Gevers; Felipe Lumbreras |
|
| |
Title |
Combining Priors, Appearance and Context for Road Detection |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
|
| |
Volume |
15 |
Issue |
3 |
Pages |
1168-1178 |
|
| |
Keywords |
Illuminant invariance; lane markings; road detection; road prior; road scene understanding; vanishing point; 3-D scene layout |
|
| |
Abstract |
Detecting the free road surface ahead of a moving vehicle is an important research topic in different areas of computer vision, such as autonomous driving or car collision warning.
Current vision-based road detection methods are usually based solely on low-level features. Furthermore, they generally assume structured roads, road homogeneity, and uniform lighting conditions, constraining their applicability in real-world scenarios. In this paper, road priors and contextual information are introduced for road detection. First, we propose an algorithm to estimate road priors online using geographical information, providing relevant initial information about the road location. Then, contextual cues, including horizon lines, vanishing points, lane markings, 3-D scene layout, and road geometry, are used in addition to low-level cues derived from the appearance of roads. Finally, a generative model is used to combine these cues and priors, leading to a road detection method that is, to a large degree, robust to varying imaging conditions, road types, and scenarios. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1524-9050 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS; 600.076;ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ALG2014 |
Serial |
2501 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu |
|
| |
Title |
Combining where and what in change detection for unsupervised foreground learning in surveillance |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
48 |
Issue |
3 |
Pages |
709-719 |
|
| |
Keywords |
Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance |
|
| |
Abstract |
Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art. |
|
| |
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 |
ISE; 600.063; 600.078 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HPG2015 |
Serial |
2589 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Noha Elfiky; Jordi Gonzalez; Xavier Roca |
|
| |
Title |
Compact and Adaptive Spatial Pyramids for Scene Recognition |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
|
| |
Volume |
30 |
Issue |
8 |
Pages |
492–500 |
|
| |
Keywords |
|
|
| |
Abstract |
Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets. |
|
| |
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 |
ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ EGR2012 |
Serial |
2004 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Arjan Gijsenji; Theo Gevers; Joost Van de Weijer |
|
| |
Title |
Computational Color Constancy: Survey and Experiments |
Type |
Journal Article |
| |
Year |
2011 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
|
| |
Volume |
20 |
Issue |
9 |
Pages |
2475-2489 |
|
| |
Keywords |
computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting |
|
| |
Abstract |
Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets. |
|
| |
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 |
1057-7149 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE;CIC |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGW2011 |
Serial |
1717 |
|
| Permanent link to this record |
