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| Author | Sergio Escalera | ||||
| Title | Multi-Modal Human Behaviour Analysis from Visual Data Sources | Type | Journal | ||
| Year | 2013 | Publication | ERCIM News journal | Abbreviated Journal | ERCIM |
| Volume | 95 | Issue | Pages | 21-22 | |
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| Abstract | 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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| Series Volume | Series Issue | Edition | |||
| ISSN | 0926-4981 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ Esc2013 | Serial  |
2361 | ||
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| Author | Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-class Pattern and Object Recognition A | Type | Book Whole | ||
| Year | 2008 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Many real problems require multi-class decisions. In the Pattern Recognition field,
many techniques have been proposed to deal with the binary problem. However, the extension of many 2-class classifiers to the multi-class case is a hard task. In this sense, Error-Correcting Output Codes (ECOC) demonstrated to be a powerful tool to combine any number of binary classifiers to model multi-class problems. But there are still many open issues about the capabilities of the ECOC framework. In this thesis, the two main stages of an ECOC design are analyzed: the coding and the decoding steps. We present different problem-dependent designs. These designs take advantage of the knowledge of the problem domain to minimize the number of classifiers, obtaining a high classification performance. On the other hand, we analyze the ECOC codification in order to define new decoding rules that take full benefit from the information provided at the coding step. Moreover, as a successful classification requires a rich feature set, new feature detection/extraction techniques are presented and evaluated on the new ECOC designs. The evaluation of the new methodology is performed on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, Intravascular Ultrasound images, Caltech Repository data set or Chaga’s disease data set. The results of this thesis show that significant performance improvements are obtained on both traditional coding and decoding ECOC designs when the new coding and decoding rules are taken into account. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Petia Radeva;Oriol Pujol | |
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2008b | Serial |
2217 | ||
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| Author | Sergio Escalera | ||||
| Title | Human Behavior Analysis From Depth Maps | Type | Conference Article | ||
| Year | 2012 | Publication | 7th Conference on Articulated Motion and Deformable Objects | Abbreviated Journal | |
| Volume | 7378 | Issue | Pages | 282-292 | |
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| Abstract | Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed. | ||||
| Address | Mallorca | ||||
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| Publisher | Springer Heidelberg | Place of Publication | Editor | F.J. Perales; R.B. Fisher; T.B. Moeslund | |
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| ISSN | 0302-9743 | ISBN | 978-3-642-31566-4 | Medium | |
| Area | Expedition | Conference | AMDO | ||
| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | Admin @ si @ Esc2012 | Serial |
2040 | ||
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| Author | Sergio Escalera | ||||
| Title | Coding and Decoding Design of ECOCs for Multi-Class Pattern and Object Recognition | Type | Miscellaneous | ||
| Year | 2008 | Publication | Abbreviated Journal | ||
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Esc2008a | Serial |
1106 | ||
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| Author | Sergio Escalera | ||||
| Title | Fast traffic model matching and recognition on gray-scale images | Type | Report | ||
| Year | 2005 | Publication | CVC Technical Report #84 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | MILAB; HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ Esc2005 | Serial |
572 | ||
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