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Author |
Q. Bao; Marçal Rusiñol; M.Coustaty; Muhammad Muzzamil Luqman; C.D. Tran; Jean-Marc Ogier |
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Title |
Delaunay triangulation-based features for Camera-based document image retrieval system |
Type |
Conference Article |
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Year |
2016 |
Publication |
12th IAPR Workshop on Document Analysis Systems |
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Pages |
1-6 |
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Keywords |
Camera-based Document Image Retrieval; Delaunay Triangulation; Feature descriptors; Indexing |
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Abstract |
In this paper, we propose a new feature vector, named DElaunay TRIangulation-based Features (DETRIF), for real-time camera-based document image retrieval. DETRIF is computed based on the geometrical constraints from each pair of adjacency triangles in delaunay triangulation which is constructed from centroids of connected components. Besides, we employ a hashing-based indexing system in order to evaluate the performance of DETRIF and to compare it with other systems such as LLAH and SRIF. The experimentation is carried out on two datasets comprising of 400 heterogeneous-content complex linguistic map images (huge size, 9800 X 11768 pixels resolution)and 700 textual document images. |
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Santorini; Greece; April 2016 |
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DAS |
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DAG; 600.061; 600.084; 600.077 |
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no |
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Admin @ si @ BRC2016 |
Serial |
2757 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva; Jordi Vitria |
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Title |
A First Approach to Activity Recognition Using Topic Models |
Type |
Conference Article |
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Year |
2009 |
Publication |
12th International Conference of the Catalan Association for Artificial Intelligence |
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Volume |
202 |
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Pages |
74 - 82 |
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In this work, we present a first approach to activity patterns discovery by mean of topic models. Using motion data collected with a wearable device we prototype, TheBadge, we analyse raw accelerometer data using Latent Dirichlet Allocation (LDA), a particular instantiation of topic models. Results show that for particular values of the parameters necessary for applying LDA to a countinous dataset, good accuracies in activity classification can be achieved. |
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Cardona, Spain |
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978-1-60750-061-2 |
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CCIA |
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Notes |
OR;MILAB;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ CPR2009e |
Serial |
1231 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Face-to-face social activity detection using data collected with a wearable device |
Type |
Conference Article |
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Year |
2009 |
Publication |
4th Iberian Conference on Pattern Recognition and Image Analysis |
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5524 |
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56–63 |
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In this work the feasibility of building a socially aware badge that learns from user activities is explored. A wearable multisensor device has been prototyped for collecting data about user movements and photos of the environment where the user acts. Using motion data, speaking and other activities have been classified. Images have been analysed in order to complement motion data and help for the detection of social behaviours. A face detector and an activity classifier are both used for detecting if users have a social activity in the time they worn the device. Good results encourage the improvement of the system at both hardware and software level |
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Póvoa de Varzim, Portugal |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-02171-8 |
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IbPRIA |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ CPR2009b |
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1206 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Embedding Random Projections in Regularized Gradient Boosting Machines |
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Conference Article |
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Year |
2010 |
Publication |
Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases |
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44–53 |
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Barcelona (Spain) |
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SUEMA |
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Notes |
MILAB;HUPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ CPR2010c |
Serial |
1466 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Classyfing Agitation in Sedated ICU Patients |
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Conference Article |
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Year |
2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
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Pages |
19–20 |
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Agitation is a serious problem in sedated intensive care unit (ICU) patients. In this work, standard machine learning techniques working on wearable accelerometer data have been used to classifying agitation levels achieving very good classification performances. |
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Girona |
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MICCAT |
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Notes |
MILAB;HUPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ COR2010 |
Serial |
1467 |
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Permanent link to this record |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Personalization and User Verification in Wearable Systems using Biometric Walking Patterns |
Type |
Journal Article |
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Year |
2012 |
Publication |
Personal and Ubiquitous Computing |
Abbreviated Journal |
PUC |
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Volume |
16 |
Issue |
5 |
Pages |
563-580 |
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Abstract |
In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies. |
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Springer-Verlag |
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1617-4909 |
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MILAB;HuPBA |
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no |
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Admin @ si @ CPR2012 |
Serial |
1706 |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Human Activity Recognition from Accelerometer Data using a Wearable Device |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
6669 |
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Pages |
289-296 |
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Abstract |
Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up to 94%. |
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Las Palmas de Gran Canaria. Spain |
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Springer Berlin Heidelberg |
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Editor |
Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario |
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LNCS |
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0302-9743 |
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978-3-642-21256-7 |
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IbPRIA |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ CPR2011a |
Serial |
1735 |
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Permanent link to this record |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Approximate Convex Hulls Family for One-Class Cassification |
Type |
Conference Article |
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Year |
2011 |
Publication |
10th International Workshop on Multiple Classifier Systems |
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Volume |
6713 |
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Pages |
106-115 |
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In this work, a new method for one-class classification based on the Convex Hull geometric structure is proposed. The new method creates a family of convex hulls able to fit the geometrical shape of the training points. The increased computational cost due to the creation of the convex hull in multiple dimensions is circumvented using random projections. This provides an approximation of the original structure with multiple bi-dimensional views. In the projection planes, a mechanism for noisy points rejection has also been elaborated and evaluated. Results show that the approach performs considerably well with respect to the state the art in one-class classification. |
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Napoli, Italy |
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Springer Berlin Heidelberg |
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Editor |
Carlo Sansone; Josef Kittler; Fabio Roli |
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LNCS |
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0302-9743 |
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978-3-642-21556-8 |
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MCS |
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MILAB;HuPBA |
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no |
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Admin @ si @ CPR2011b |
Serial |
1761 |
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Permanent link to this record |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
User Verification From Walking Activity. First Steps Towards a Personal Verification System |
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Conference Article |
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2011 |
Publication |
1st International Conference on Pervasive and Embedded Computing and Communication Systems |
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Algarve, Portugal |
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PECCS |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ CPR2011c |
Serial |
1762 |
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Permanent link to this record |
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Author |
Pierluigi Casale; Oriol Pujol; Petia Radeva |
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Title |
Approximate polytope ensemble for one-class classification |
Type |
Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
2 |
Pages |
854-864 |
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One-class classification; Convex hull; High-dimensionality; Random projections; Ensemble learning |
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Abstract |
In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets. |
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Notes |
MILAB; 605.203 |
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no |
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Call Number |
Admin @ si @ CPR2014a |
Serial |
2469 |
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Author |
Pierluigi Casale |
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Title |
Social Environment Description from Data Collected with a Wearable Device |
Type |
Miscellaneous |
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Year |
2008 |
Publication |
CVC Technical Report #124 |
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Barcelona, Spain |
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no |
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Admin @ si @ Cas2008 |
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1151 |
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Author |
Pierluigi Casale |
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Title |
Approximate Ensemble Methods for Physical Activity Recognition Applications |
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Book Whole |
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Year |
2011 |
Publication |
PhD Thesis, Universitat de Barcelona-CVC |
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The main interest of this thesis focuses on computational methodologies able to
reduce the degree of complexity of learning algorithms and its application to physical
activity recognition.
Random Projections will be used to reduce the computational complexity in Multiple Classifier Systems. A new boosting algorithm and a new one-class classification
methodology have been developed. In both cases, random projections are used for
reducing the dimensionality of the problem and for generating diversity, exploiting in
this way the benefits that ensembles of classifiers provide in terms of performances
and stability. Moreover, the new one-class classification methodology, based on an ensemble strategy able to approximate a multidimensional convex-hull, has been proved
to over-perform state-of-the-art one-class classification methodologies.
The practical focus of the thesis is towards Physical Activity Recognition. A new
hardware platform for wearable computing application has been developed and used
for collecting data of activities of daily living allowing to study the optimal features
set able to successful classify activities.
Based on the classification methodologies developed and the study conducted on
physical activity classification, a machine learning architecture capable to provide a
continuous authentication mechanism for mobile-devices users has been worked out,
as last part of the thesis. The system, based on a personalized classifier, states on
the analysis of the characteristic gait patterns typical of each individual ensuring an
unobtrusive and continuous authentication mechanism |
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Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Oriol Pujol;Petia Radeva |
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MILAB |
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no |
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Admin @ si @ Cas2011 |
Serial |
1837 |
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Permanent link to this record |
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Author |
Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno |
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Title |
Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" |
Type |
Conference Article |
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Year |
2017 |
Publication |
Workshop on Big Data Analytics and Machine Learning for Data Communication Networks |
Abbreviated Journal |
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43-48 |
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Keywords |
mobile networks; call detail records; human mobility |
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Abstract |
The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes,
have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users.
By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “
always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations. |
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UCLA; USA; August 2017 |
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978-1-4503-5054-9 |
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ACMW (SIGCOMM) |
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Notes |
HuPBA; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ FPT2017 |
Serial |
2980 |
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Permanent link to this record |
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Author |
Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera |
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Title |
RGB-D-based Human Motion Recognition with Deep Learning: A Survey |
Type |
Journal Article |
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Year |
2018 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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Volume |
171 |
Issue |
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Pages |
118-139 |
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Keywords |
Human motion recognition; RGB-D data; Deep learning; Survey |
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Abstract |
Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research. |
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HUPBA; no proj |
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Admin @ si @ WLO2018 |
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3123 |
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Philippe Dosch; Josep Llados |
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Vectorial Signatures for Symbol Discrimination |
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2003 |
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Proceedings of Fifth IAPR International Workshop on Graphics Recognition, 159–169 |
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Barcelona |
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DAG @ dag @ DoL2003 |
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373 |
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