|
Records |
Links |
|
Author |
Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera |
|
|
Title |
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD |
Type |
Journal Article |
|
Year |
2016 |
Publication |
IEEE Transactions on System, Man and Cybernetics, Part B |
Abbreviated Journal |
TSMCB |
|
|
Volume |
46 |
Issue |
1 |
Pages |
136-147 |
|
|
Keywords |
Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data |
|
|
Abstract |
We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. |
|
|
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; MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ BHE2016 |
Serial |
2566 |
|
Permanent link to this record |
|
|
|
|
Author |
Isabelle Guyon; Imad Chaabane; Hugo Jair Escalante; Sergio Escalera; Damir Jajetic; James Robert Lloyd; Nuria Macia; Bisakha Ray; Lukasz Romaszko; Michele Sebag; Alexander Statnikov; Sebastien Treguer; Evelyne Viegas |
|
|
Title |
A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention |
Type |
Conference Article |
|
Year |
2016 |
Publication |
AutoML Workshop |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
1 |
Pages |
1-8 |
|
|
Keywords |
AutoML Challenge; machine learning; model selection; meta-learning; repre- sentation learning; active learning |
|
|
Abstract |
The ChaLearn AutoML Challenge team conducted a large scale evaluation of fully automatic, black-box learning machines for feature-based classification and regression problems. The test bed was composed of 30 data sets from a wide variety of application domains and ranged across different types of complexity. Over six rounds, participants succeeded in delivering AutoML software capable of being trained and tested without human intervention. Although improvements can still be made to close the gap between human-tweaked and AutoML models, this competition contributes to the development of fully automated environments by challenging practitioners to solve problems under specific constraints and sharing their approaches; the platform will remain available for post-challenge submissions at http://codalab.org/AutoML. |
|
|
Address |
New York; USA; June 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 |
ICML |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ GCE2016 |
Serial |
2769 |
|
Permanent link to this record |
|
|
|
|
Author |
Pejman Rasti; Tonis Uiboupin; Sergio Escalera; Gholamreza Anbarjafari |
|
|
Title |
Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring |
Type |
Conference Article |
|
Year |
2016 |
Publication |
9th Conference on Articulated Motion and Deformable Objects |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Palma de Mallorca; Spain; July 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 |
AMDO |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ RUE2016 |
Serial |
2846 |
|
Permanent link to this record |
|
|
|
|
Author |
Dennis H. Lundtoft; Kamal Nasrollahi; Thomas B. Moeslund; Sergio Escalera |
|
|
Title |
Spatiotemporal Facial Super-Pixels for Pain Detection |
Type |
Conference Article |
|
Year |
2016 |
Publication |
9th Conference on Articulated Motion and Deformable Objects |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Facial images; Super-pixels; Spatiotemporal filters; Pain detection |
|
|
Abstract |
Best student paper award.
Pain detection using facial images is of critical importance in many Health applications. Since pain is a spatiotemporal process, recent works on this topic employ facial spatiotemporal features to detect pain. These systems extract such features from the entire area of the face. In this paper, we show that by employing super-pixels we can divide the face into three regions, in a way that only one of these regions (about one third of the face) contributes to the pain estimation and the other two regions can be discarded. The experimental results on the UNBCMcMaster database show that the proposed system using this single region outperforms state-of-the-art systems in detecting no-pain scenarios, while it reaches comparable results in detecting weak and severe pain scenarios. |
|
|
Address |
Palma de Mallorca; Spain; July 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 |
AMDO |
|
|
Notes |
HUPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ LNM2016 |
Serial |
2847 |
|
Permanent link to this record |
|
|
|
|
Author |
Mark Philip Philipsen; Anders Jorgensen; Thomas B. Moeslund; Sergio Escalera |
|
|
Title |
RGB-D Segmentation of Poultry Entrails |
Type |
Conference Article |
|
Year |
2016 |
Publication |
9th Conference on Articulated Motion and Deformable Objects |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Best commercial paper award. |
|
|
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 |
AMDO |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ PJM2016 |
Serial |
2848 |
|
Permanent link to this record |
|
|
|
|
Author |
Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera |
|
|
Title |
Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal |
Type |
Conference Article |
|
Year |
2016 |
Publication |
4th Congreso WCPT-SAR |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
|
|
|
Address |
Buenos Aires; Argentina; June 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 |
WCPT-SAR |
|
|
Notes |
HuPBA;MILAB |
Approved |
no |
|
|
Call Number |
Admin @ si @ RRR2016 |
Serial |
2853 |
|
Permanent link to this record |
|
|
|
|
Author |
Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera |
|
|
Title |
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History |
Type |
Journal Article |
|
Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
|
Volume |
28 |
Issue |
8 |
Pages |
1548-1568 |
|
|
Keywords |
Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal |
|
|
Abstract |
Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. |
|
|
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;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ COC2016 |
Serial |
2718 |
|
Permanent link to this record |
|
|
|
|
Author |
Antonio Hernandez; Sergio Escalera; Stan Sclaroff |
|
|
Title |
Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures |
Type |
Journal Article |
|
Year |
2016 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
|
|
Volume |
118 |
Issue |
1 |
Pages |
49–64 |
|
|
Keywords |
Contextual rescoring; Poselets; Human pose estimation |
|
|
Abstract |
In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer US |
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
0920-5691 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes |
HuPBA;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ HES2016 |
Serial |
2719 |
|
Permanent link to this record |
|
|
|
|
Author |
Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria |
|
|
Title |
Automatic garment retexturing based on infrared information |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
|
|
Volume |
59 |
Issue |
|
Pages |
28-38 |
|
|
Keywords |
Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading |
|
|
Abstract |
This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Elsevier |
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;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ ADT2016 |
Serial |
2759 |
|
Permanent link to this record |
|
|
|
|
Author |
Marc Oliu; Ciprian Corneanu; Laszlo A. Jeni; Jeffrey F. Cohn; Takeo Kanade; Sergio Escalera |
|
|
Title |
Continuous Supervised Descent Method for Facial Landmark Localisation |
Type |
Conference Article |
|
Year |
2016 |
Publication |
13th Asian Conference on Computer Vision |
Abbreviated Journal |
|
|
|
Volume |
10112 |
Issue |
|
Pages |
121-135 |
|
|
Keywords |
|
|
|
Abstract |
Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size. |
|
|
Address |
Taipei; Taiwan; November 2016 |
|
|
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 |
ACCV |
|
|
Notes |
HuPBA;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ OCJ2016 |
Serial |
2838 |
|
Permanent link to this record |
|
|
|
|
Author |
Sergio Escalera; Vassilis Athitsos; Isabelle Guyon |
|
|
Title |
Challenges in multimodal gesture recognition |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Journal of Machine Learning Research |
Abbreviated Journal |
JMLR |
|
|
Volume |
17 |
Issue |
|
Pages |
1-54 |
|
|
Keywords |
Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM |
|
|
Abstract |
This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
Zhuowen Tu |
|
|
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;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ EAG2016 |
Serial |
2764 |
|
Permanent link to this record |
|
|
|
|
Author |
Pejman Rasti; Salma Samiei; Mary Agoyi; Sergio Escalera; Gholamreza Anbarjafari |
|
|
Title |
Robust non-blind color video watermarking using QR decomposition and entropy analysis |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
|
|
Volume |
38 |
Issue |
|
Pages |
838-847 |
|
|
Keywords |
Video watermarking; QR decomposition; Discrete Wavelet Transformation; Chirp Z-transform; Singular value decomposition; Orthogonal–triangular decomposition |
|
|
Abstract |
Issues such as content identification, document and image security, audience measurement, ownership and copyright among others can be settled by the use of digital watermarking. Many recent video watermarking methods show drops in visual quality of the sequences. The present work addresses the aforementioned issue by introducing a robust and imperceptible non-blind color video frame watermarking algorithm. The method divides frames into moving and non-moving parts. The non-moving part of each color channel is processed separately using a block-based watermarking scheme. Blocks with an entropy lower than the average entropy of all blocks are subject to a further process for embedding the watermark image. Finally a watermarked frame is generated by adding moving parts to it. Several signal processing attacks are applied to each watermarked frame in order to perform experiments and are compared with some recent algorithms. Experimental results show that the proposed scheme is imperceptible and robust against common signal processing attacks. |
|
|
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;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @RSA2016 |
Serial |
2766 |
|
Permanent link to this record |
|
|
|
|
Author |
Cristina Palmero; Albert Clapes; Chris Bahnsen; Andreas Møgelmose; Thomas B. Moeslund; Sergio Escalera |
|
|
Title |
Multi-modal RGB-Depth-Thermal Human Body Segmentation |
Type |
Journal Article |
|
Year |
2016 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
|
|
Volume |
118 |
Issue |
2 |
Pages |
217-239 |
|
|
Keywords |
Human body segmentation; RGB ; Depth Thermal |
|
|
Abstract |
This work addresses the problem of human body segmentation from multi-modal visual cues as a first stage of automatic human behavior analysis. We propose a novel RGB–depth–thermal dataset along with a multi-modal segmentation baseline. The several modalities are registered using a calibration device and a registration algorithm. Our baseline extracts regions of interest using background subtraction, defines a partitioning of the foreground regions into cells, computes a set of image features on those cells using different state-of-the-art feature extractions, and models the distribution of the descriptors per cell using probabilistic models. A supervised learning algorithm then fuses the output likelihoods over cells in a stacked feature vector representation. The baseline, using Gaussian mixture models for the probabilistic modeling and Random Forest for the stacked learning, is superior to other state-of-the-art methods, obtaining an overlap above 75 % on the novel dataset when compared to the manually annotated ground-truth of human segmentations. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Springer US |
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;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ PCB2016 |
Serial |
2767 |
|
Permanent link to this record |
|
|
|
|
Author |
Gerard Canal; Sergio Escalera; Cecilio Angulo |
|
|
Title |
A Real-time Human-Robot Interaction system based on gestures for assistive scenarios |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
|
|
Volume |
149 |
Issue |
|
Pages |
65-77 |
|
|
Keywords |
Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation |
|
|
Abstract |
Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Elsevier B.V. |
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;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ CEA2016 |
Serial |
2768 |
|
Permanent link to this record |
|
|
|
|
Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
|
|
Title |
Action Recognition by Pairwise Proximity Function Support Vector Machines with Dynamic Time Warping Kernels |
Type |
Conference Article |
|
Year |
2016 |
Publication |
29th Canadian Conference on Artificial Intelligence |
Abbreviated Journal |
|
|
|
Volume |
9673 |
Issue |
|
Pages |
3-14 |
|
|
Keywords |
|
|
|
Abstract |
In the context of human action recognition using skeleton data, the 3D trajectories of joint points may be considered as multi-dimensional time series. The traditional recognition technique in the literature is based on time series dis(similarity) measures (such as Dynamic Time Warping). For these general dis(similarity) measures, k-nearest neighbor algorithms are a natural choice. However, k-NN classifiers are known to be sensitive to noise and outliers. In this paper, a new class of Support Vector Machine that is applicable to trajectory classification, such as action recognition, is developed by incorporating an efficient time-series distances measure into the kernel function. More specifically, the derivative of Dynamic Time Warping (DTW) distance measure is employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite (PSD) kernels in the SVM formulation. The recognition results of the proposed technique on two action recognition datasets demonstrates the ourperformance of our methodology compared to the state-of-the-art methods. Remarkably, we obtained 89 % accuracy on the well-known MSRAction3D dataset using only 3D trajectories of body joints obtained by Kinect |
|
|
Address |
Victoria; Canada; May 2016 |
|
|
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 |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
AI |
|
|
Notes |
HuPBA;MILAB; |
Approved |
no |
|
|
Call Number |
Admin @ si @ BGE2016b |
Serial |
2770 |
|
Permanent link to this record |