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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 |
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Journal Article |
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2018 |
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Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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171 |
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118-139 |
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Human motion recognition; RGB-D data; Deep learning; Survey |
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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;MILAB |
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no |
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Admin @ si @ WLO2018 |
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3123 |
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Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Integrating Vision and Language for First Impression Personality Analysis |
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Journal Article |
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Year |
2018 |
Publication |
IEEE Multimedia |
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MULTIMEDIA |
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25 |
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2 |
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24 - 33 |
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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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HUPBA; 602.133;MILAB |
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no |
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Admin @ si @ GAL2018 |
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3124 |
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Albert Clapes; Alex Pardo; Oriol Pujol; Sergio Escalera |
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Title |
Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly |
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Journal Article |
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Year |
2018 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVAP |
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29 |
Issue |
5 |
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765–788 |
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Multimodal activity detection; Computer vision; Inertial sensors; Dense trajectories; Dynamic time warping; Assistive technology |
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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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HUPBA; no proj;MILAB |
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no |
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Admin @ si @ CPP2018 |
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3125 |
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Jun Wan; Sergio Escalera; Francisco Perales; Josef Kittler |
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Title |
Articulated Motion and Deformable Objects |
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Journal Article |
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Year |
2018 |
Publication |
Pattern Recognition |
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PR |
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79 |
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55-64 |
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This guest editorial introduces the twenty two papers accepted for this Special Issue on Articulated Motion and Deformable Objects (AMDO). They are grouped into four main categories within the field of AMDO: human motion analysis (action/gesture), human pose estimation, deformable shape segmentation, and face analysis. For each of the four topics, a survey of the recent developments in the field is presented. The accepted papers are briefly introduced in the context of this survey. They contribute novel methods, algorithms with improved performance as measured on benchmarking datasets, as well as two new datasets for hand action detection and human posture analysis. The special issue should be of high relevance to the reader interested in AMDO recognition and promote future research directions in the field. |
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HUPBA; no proj;MILAB |
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no |
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Admin @ si @ WEP2018 |
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3126 |
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Author |
Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera |
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Title |
Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification |
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Journal Article |
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Year |
2018 |
Publication |
Image and Vision Computing |
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IMAVIS |
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79 |
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76-85 |
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Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset. |
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HuPBA; 602.143;MV;OR;MILAB |
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no |
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Admin @ si @ JBE2018 |
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3138 |
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