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Thomas B. Moeslund; Sergio Escalera; Gholamreza Anbarjafari; Kamal Nasrollahi; Jun Wan |
![goto web page url](img/www.gif)
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Title |
Statistical Machine Learning for Human Behaviour Analysis |
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Journal Article |
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2020 |
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Entropy |
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ENTROPY |
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25 |
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5 |
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530 |
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action recognition; emotion recognition; privacy-aware |
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HuPBA; no proj |
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Admin @ si @ MEA2020 |
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3441 |
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Author |
Francesc Tanarro Marquez; Pau Gratacos Marti; F. Javier Sanchez; Joan Ramon Jimenez Minguell; Coen Antens; Enric Sala i Esteva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A device for monitoring condition of a railway supply |
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Patent |
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Year |
2012 |
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EP 2 404 777 A1 |
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of a railway supply line when the supply line is in contact with a head of a pantograph of a vehicle in order to power said vehicle . The device includes a camera ( for monitoring parameters indicative of operating capability of said supply line.
The device is intended to monitor condition
tive of operating capability of said supply line. The device includes a reflective element. comprising a pattern , intended to be arranged onto the pantograph head . The camera is intended to be arranged on the vehicle (10) so as to register the pattern position regarding a vertical direction. |
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ALSTOM Transport SA |
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European Patent Office |
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MV |
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IAM @ iam @ MMS2012 |
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1854 |
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Author |
Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Multi-Modal Human Behaviour Analysis from Visual Data Sources |
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Journal |
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2013 |
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ERCIM News journal |
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ERCIM |
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95 |
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21-22 |
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The Human Pose Recovery and Behaviour Analysis group (HuPBA), University of Barcelona, is developing a line of research on multi-modal analysis of humans in visual data. The novel technology is being applied in several scenarios with high social impact, including sign language recognition, assisted technology and supported diagnosis for the elderly and people with mental/physical disabilities, fitness conditioning, and Human Computer Interaction. |
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0926-4981 |
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HuPBA;MILAB |
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Admin @ si @ Esc2013 |
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2361 |
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Author |
Debora Gil; Aura Hernandez-Sabate; Antoni Carol; Oriol Rodriguez; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Deterministic-Statistic Adventitia Detection in IVUS Images |
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Conference Article |
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2005 |
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ESC Congress |
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Electron microscopy; Unbending; 2D crystal; Interpolation; Approximation |
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Plaque analysis in IVUS planes needs accurate intima and adventitia models. Large variety in adventitia descriptors difficulties its detection and motivates using a classification strategy for selecting points on the structure. Whatever the set of descriptors used, the selection stage suffers from fake responses due to noise and uncompleted true curves. In order to smooth background noise while strengthening responses, we apply a restricted anisotropic filter that homogenizes grey levels along the image significant structures. Candidate points are extracted by means of a simple semi supervised adaptive classification of the filtered image response to edge and calcium detectors. The final model is obtained by interpolating the former line segments with an anisotropic contour closing technique based on functional extension principles. |
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Stockholm; Sweden; September 2005 |
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IAM;MILAB |
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IAM @ iam @ RMF2005a |
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1523 |
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Author |
Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Deteccion automatica de la dominancia en conversaciones diadicas |
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Journal Article |
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2010 |
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Escritos de Psicologia |
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EP |
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3 |
Issue |
2 |
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41–45 |
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Dominance detection; Non-verbal communication; Visual features |
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Dominance is referred to the level of influence that a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on the dominance detection of visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers' opinion. Moreover, these indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analyses showed a high correlation and allows the categorization of dominant people in public discussion video sequences. |
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1989-3809 |
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HUPBA; OR; MILAB;MV |
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BCNPCL @ bcnpcl @ EMV2010 |
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1315 |
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Author |
David Vazquez; Antonio Lopez |
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Title |
Intrusion Classification in Intelligent Video Surveillance Systems |
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Report |
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2008 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
Estudis d'Enginyeria Superior en Informática |
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UAB |
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Human detection; Car detection; Intrusion detection |
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An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system |
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Bellaterra, Spain |
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PFC |
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ADAS |
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no |
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ADAS @ adas @ VL2008a |
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1670 |
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Author |
Sergio Escalera; Josep Moya; Laura Igual; Veronica Violant; Maria Teresa Anguera |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Automatic Human Behavior Analysis in ADHD |
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Conference Article |
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2012 |
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Eunethydis 2nd International ADHD Conference |
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Poster |
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EUNETHYDIS |
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MILAB;HuPBA |
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no |
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Admin @ si @ EMI2012a |
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2058 |
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Author |
Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences |
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Journal Article |
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2010 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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Article ID 507247
A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes. |
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1110-8657 |
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ISE |
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ISE @ ise @ MRR2010 |
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1208 |
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Sergio Escalera; Oriol Pujol; Petia Radeva; Jordi Vitria; Maria Teresa Anguera |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Automatic Detection of Dominance and Expected Interest |
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Journal Article |
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2010 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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12 |
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Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems. |
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1110-8657 |
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OR;MILAB;HUPBA;MV |
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BCNPCL @ bcnpcl @ EPR2010d |
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1283 |
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Author |
Ariel Amato; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns |
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Journal Article |
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2010 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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7 |
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Article ID 901205
This paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns, which are formed by similarity measurement between two sets of RGB color vectors: one belonging to the background image and the other to the current image. We show how these patterns are used to improve foreground detection in the presence of moving shadows and in the case when there are strong similarities in color between background and foreground pixels. Experimental results over a collection of public and own datasets of real image sequences demonstrate that the proposed technique achieves a superior performance compared with state-of-the-art methods. Furthermore, both the low computational and space complexities make the presented algorithm feasible for real-time applications. |
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1110-8657 |
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ISE @ ise @ AMR2010 |
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1463 |
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Author |
Wenjuan Gong; Jordi Gonzalez; Xavier Roca |
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Title |
Human Action Recognition based on Estimated Weak Poses |
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Journal Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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We present a novel method for human action recognition (HAR) based on estimated poses from image sequences. We use 3D human pose data as additional information and propose a compact human pose representation, called a weak pose, in a low-dimensional space while still keeping the most discriminative information for a given pose. With predicted poses from image features, we map the problem from image feature space to pose space, where a Bag of Poses (BOP) model is learned for the final goal of HAR. The BOP model is a modified version of the classical bag of words pipeline by building the vocabulary based on the most representative weak poses for a given action. Compared with the standard k-means clustering, our vocabulary selection criteria is proven to be more efficient and robust against the inherent challenges of action recognition. Moreover, since for action recognition the ordering of the poses is discriminative, the BOP model incorporates temporal information: in essence, groups of consecutive poses are considered together when computing the vocabulary and assignment. We tested our method on two well-known datasets: HumanEva and IXMAS, to demonstrate that weak poses aid to improve action recognition accuracies. The proposed method is scene-independent and is comparable with the state-of-art method. |
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Admin @ si @ GGR2012 |
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2003 |
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Author |
Laura Igual; Agata Lapedriza; Ricard Borras |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Robust Gait-Based Gender Classification using Depth Cameras |
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Journal Article |
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2013 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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37 |
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1 |
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72-80 |
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This article presents a new approach for gait-based gender recognition using depth cameras, that can run in real time. The main contribution of this study is a new fast feature extraction strategy that uses the 3D point cloud obtained from the frames in a gait cycle. For each frame, these points are aligned according to their centroid and grouped. After that, they are projected into their PCA plane, obtaining a representation of the cycle particularly robust against view changes. Then, final discriminative features are computed by first making a histogram of the projected points and then using linear discriminant analysis. To test the method we have used the DGait database, which is currently the only publicly available database for gait analysis that includes depth information. We have performed experiments on manually labeled cycles and over whole video sequences, and the results show that our method improves the accuracy significantly, compared with state-of-the-art systems which do not use depth information. Furthermore, our approach is insensitive to illumination changes, given that it discards the RGB information. That makes the method especially suitable for real applications, as illustrated in the last part of the experiments section. |
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MILAB; OR;MV |
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Admin @ si @ ILB2013 |
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2144 |
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Author |
Matthias S. Keil; Jordi Vitria |
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Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control |
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2007 |
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BCNPCL @ bcnpcl @ KeV2007 |
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794 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Detection of Complex Salient Regions |
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2008 |
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EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ EPR2008b |
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960 |
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David Masip; M. Bressan; Jordi Vitria |
![find record details (via OpenURL) openurl](img/xref.gif)
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Feature extraction methods for real-time face detection and classification |
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2005 |
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Eurasip Journal on Applied Signal Processing, 13: 2061–2071 |
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BCNPCL @ bcnpcl @ MBV2005 |
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612 |
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