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Author Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera edit  doi
openurl 
  Title Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps Type Journal Article
  Year (down) 2019 Publication IEEE Transactions on Circuits and Systems for Video Technology Abbreviated Journal TCSVT  
  Volume 29 Issue 6 Pages 1729-1740  
  Keywords Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding  
  Abstract Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset.  
  Address June 2019,  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ AAK2018 Serial 3213  
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Author Albert Clapes; Alex Pardo; Oriol Pujol; Sergio Escalera edit   pdf
url  openurl
  Title Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly Type Journal Article
  Year (down) 2018 Publication Machine Vision and Applications Abbreviated Journal MVAP  
  Volume 29 Issue 5 Pages 765–788  
  Keywords Multimodal activity detection; Computer vision; Inertial sensors; Dense trajectories; Dynamic time warping; Assistive technology  
  Abstract We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF- 2 kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ CPP2018 Serial 3125  
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Author Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya edit  openurl
  Title Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” Type Journal
  Year (down) 2018 Publication Informaciones Psiquiatricas Abbreviated Journal  
  Volume 232 Issue Pages 47-59  
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  ISSN 0210-7279 ISBN Medium  
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  Notes HUPBA; no menciona Approved no  
  Call Number Admin @ si @ FAA2018 Serial 3214  
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Author Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund edit  url
doi  openurl
  Title Back-dropout Transfer Learning for Action Recognition Type Journal Article
  Year (down) 2018 Publication IET Computer Vision Abbreviated Journal IETCV  
  Volume 12 Issue 4 Pages 484-491  
  Keywords Learning (artificial intelligence); Pattern Recognition  
  Abstract Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.  
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  Notes HUPBA; no proj Approved no  
  Call Number Admin @ si @ RKM2018 Serial 3071  
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Author Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari edit  doi
openurl 
  Title Integrating Vision and Language for First Impression Personality Analysis Type Journal Article
  Year (down) 2018 Publication IEEE Multimedia Abbreviated Journal MULTIMEDIA  
  Volume 25 Issue 2 Pages 24 - 33  
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  Abstract The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness.  
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  Notes HUPBA; 602.133 Approved no  
  Call Number Admin @ si @ GAL2018 Serial 3124  
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