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Sergio Escalera; Jordi Gonzalez; Xavier Baro; Fernando Alonso; Martha Mackay |
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Title |
Care Respite: a remote monitoring eHealth system for improving ambient assisted living |
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Conference Article |
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2016 |
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Human Motion Analysis for Healthcare Applications |
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Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery.
In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions.
This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations. |
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Savoy Place; London; uk; May 2016 |
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HMAHA |
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HuPBA; ISE; |
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Admin @ si @ EGB2016 |
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2852 |
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Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez |
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Title |
GPU-based pedestrian detection for autonomous driving |
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2016 |
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GPU Technology Conference |
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Pedestrian Detection; GPU |
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Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results. |
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Silicon Valley; San Francisco; USA; April 2016 |
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GTC |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ CSM2016 |
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2737 |
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Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez |
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Title |
Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching |
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2016 |
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GPU Technology Conference |
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Stereo; Autonomous Driving; GPU; 3d reconstruction |
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Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms |
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Silicon Valley; San Francisco; USA; April 2016 |
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ADAS; 600.085; 600.082; 600.076 |
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ADAS @ adas @ HME2016 |
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2738 |
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Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio |
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Title |
EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game |
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2016 |
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5th International Conference Games and Learning Alliance |
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10056 |
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50-59 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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GALA |
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ADAS;IAM; |
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HAC2016 |
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2864 |
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Florin Popescu; Stephane Ayache; Sergio Escalera; Xavier Baro; Cecile Capponi; Patrick Panciatici; Isabelle Guyon |
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Title |
From geospatial observations of ocean currents to causal predictors of spatio-economic activity using computer vision and machine learning |
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Conference Article |
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2016 |
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European Geosciences Union General Assembly |
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18 |
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The big data transformation currently revolutionizing science and industry forges novel possibilities in multimodal analysis scarcely imaginable only a decade ago. One of the important economic and industrial problems that stand to benefit from the recent expansion of data availability and computational prowess is the prediction of electricity demand and renewable energy generation. Both are correlates of human activity: spatiotemporal energy consumption patterns in society are a factor of both demand (weather dependent) and supply, which determine cost – a relation expected to strengthen along with increasing renewable energy dependence. One of the main drivers of European weather patterns is the activity of the Atlantic Ocean and in particular its dominant Northern Hemisphere current: the Gulf Stream. We choose this particular current as a test case in part due to larger amount of relevant data and scientific literature available for refinement of analysis techniques.
This data richness is due not only to its economic importance but also to its size being clearly visible in radar and infrared satellite imagery, which makes it easier to detect using Computer Vision (CV). The power of CV techniques makes basic analysis thus developed scalable to other smaller and less known, but still influential, currents, which are not just curves on a map, but complex, evolving, moving branching trees in 3D projected onto a 2D image.
We investigate means of extracting, from several image modalities (including recently available Copernicus radar and earlier Infrared satellites), a parameterized presentation of the state of the Gulf Stream and its environment that is useful as feature space representation in a machine learning context, in this case with the EC’s H2020-sponsored ‘See.4C’ project, in the context of which data scientists may find novel predictors of spatiotemporal energy flow. Although automated extractors of Gulf Stream position exist, they differ in methodology and result. We shall attempt to extract more complex feature representation including branching points, eddies and parameterized changes in transport and velocity. Other related predictive features will be similarly developed, such as inference of deep water flux long the current path and wider spatial scale features such as Hough transform, surface turbulence indicators and temperature gradient indexes along with multi-time scale analysis of ocean height and temperature dynamics. The geospatial imaging and ML community may therefore benefit from a baseline of open-source techniques useful and expandable to other related prediction and/or scientific analysis tasks. |
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Vienna; Austria; April 2016 |
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EGU |
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HuPBA;MV; |
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Admin @ si @ PAE2016 |
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2772 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title |
Colour Visual Coding in trained Deep Neural Networks |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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ECVP |
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CIC |
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Admin @ si @ RaV2016b |
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2895 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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NEUROBIT |
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Admin @ si @ AkP2016b |
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2900 |
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Fernando Vilariño; Dimosthenis Karatzas |
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A Living Lab approach for Citizen Science in Libraries |
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2016 |
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1st International ECSA Conference |
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Berlin; Germany; May 2016 |
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MV; DAG; 600.084; 600.097;SIAI |
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Admin @ si @ViK2016 |
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2804 |
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Author |
Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Dynamic Lexicon Generation for Natural Scene Images |
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Conference Article |
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2016 |
Publication |
14th European Conference on Computer Vision Workshops |
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395-410 |
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scene text; photo OCR; scene understanding; lexicon generation; topic modeling; CNN |
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Many scene text understanding methods approach the endtoend recognition problem from a word-spotting perspective and take huge bene t from using small per-image lexicons. Such customized lexicons are normally assumed as given and their source is rarely discussed.
In this paper we propose a method that generates contextualized lexicons
for scene images using only visual information. For this, we exploit
the correlation between visual and textual information in a dataset consisting
of images and textual content associated with them. Using the topic modeling framework to discover a set of latent topics in such a dataset allows us to re-rank a xed dictionary in a way that prioritizes the words that are more likely to appear in a given image. Moreover, we train a CNN that is able to reproduce those word rankings but using only the image raw pixels as input. We demonstrate that the quality of the automatically obtained custom lexicons is superior to a generic frequency-based baseline. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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DAG; 600.084 |
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Admin @ si @ PGR2016 |
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2825 |
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Victor Ponce; Baiyu Chen; Marc Oliu; Ciprian Corneanu; Albert Clapes; Isabelle Guyon; Xavier Baro; Hugo Jair Escalante; Sergio Escalera |
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ChaLearn LAP 2016: First Round Challenge on First Impressions – Dataset and Results |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Behavior Analysis; Personality Traits; First Impressions |
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This paper summarizes the ChaLearn Looking at People 2016 First Impressions challenge data and results obtained by the teams in the rst round of the competition. The goal of the competition was to automatically evaluate ve \apparent“ personality traits (the so-called \Big Five”) from videos of subjects speaking in front of a camera, by using human judgment. In this edition of the ChaLearn challenge, a novel data set consisting of 10,000 shorts clips from YouTube videos has been made publicly available. The ground truth for personality traits was obtained from workers of Amazon Mechanical Turk (AMT). To alleviate calibration problems between workers, we used pairwise comparisons between videos, and variable levels were reconstructed by tting a Bradley-Terry-Luce model with maximum likelihood. The CodaLab open source
platform was used for submission of predictions and scoring. The competition attracted, over a period of 2 months, 84 participants who are grouped in several teams. Nine teams entered the nal phase. Despite the diculty of the task, the teams made great advances in this round of the challenge. |
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Amsterdam; The Netherlands; October 2016 |
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HuPBA;MV; 600.063 |
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Admin @ si @ PCP2016 |
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2828 |
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Baiyu Chen; Sergio Escalera; Isabelle Guyon; Victor Ponce; N. Shah; Marc Oliu |
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Overcoming Calibration Problems in Pattern Labeling with Pairwise Ratings: Application to Personality Traits |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Calibration of labels; Label bias; Ordinal labeling; Variance Models; Bradley-Terry-Luce model; Continuous labels; Regression; Personality traits; Crowd-sourced labels |
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We address the problem of calibration of workers whose task is to label patterns with continuous variables, which arises for instance in labeling images of videos of humans with continuous traits. Worker bias is particularly dicult to evaluate and correct when many workers contribute just a few labels, a situation arising typically when labeling is crowd-sourced. In the scenario of labeling short videos of people facing a camera with personality traits, we evaluate the feasibility of the pairwise ranking method to alleviate bias problems. Workers are exposed to pairs of videos at a time and must order by preference. The variable levels are reconstructed by fitting a Bradley-Terry-Luce model with maximum likelihood. This method may at first sight, seem prohibitively expensive because for N videos, p = N (N-1)/2 pairs must be potentially processed by workers rather that N videos. However, by performing extensive simulations, we determine an empirical law for the scaling of the number of pairs needed as a function of the number of videos in order to achieve a given accuracy of score reconstruction and show that the pairwise method is a ordable. We apply the method to the labeling of a large scale dataset of 10,000 videos used in the ChaLearn Apparent Personality Trait challenge. |
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Amsterdam; The Netherlands; October 2016 |
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ECCVW |
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HuPBA;MILAB; |
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Admin @ si @ CEG2016 |
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2829 |
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Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari |
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SASE: RGB-Depth Database for Human Head Pose Estimation |
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2016 |
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14th European Conference on Computer Vision Workshops |
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Amsterdam; The Netherlands; October 2016 |
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HuPBA;MILAB; |
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Admin @ si @ LEA2016a |
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2840 |
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Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier |
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LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode |
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2016 |
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14th European Conference on Computer Vision Workshops |
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9915 |
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894-900 |
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Simulation environment; Automated Driving; Driver-Vehicle interaction |
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Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical. |
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Amsterdam; The Netherlands; October 2016 |
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ADAS;IAM; 600.085; 600.076 |
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MHE2016 |
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2865 |
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Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez |
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Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition |
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2016 |
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14th European Conference on Computer Vision |
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697-716 |
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Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos. |
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Amsterdam; The Netherlands; October 2016 |
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ADAS; 600.076; 600.085 |
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Admin @ si @ SGV2016 |
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2824 |
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Fernando Vilariño; Dan Norton; Onur Ferhat |
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The Eye Doesn't Click – Eyetracking and Digital Content Interaction |
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2016 |
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4S/EASST Conference |
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Barcelona; Spain; September 2016 |
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EASST |
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MV; 600.097;SIAI |
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Admin @ si @VNF2016 |
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2801 |
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