Full text
| |
| Author(s): |
Renata Cristina Barros Madeo
Total Authors: 1
|
| Document type: | Master's Dissertation |
| Press: | São Paulo. |
| Institution: | Universidade de São Paulo (USP). Escola de Artes, Ciências e Humanidades (EACH) |
| Defense date: | 2013-05-15 |
| Examining board members: |
Sarajane Marques Peres;
João Luiz Bernardes Junior;
Fernando José Von Zuben
|
| Advisor: | Sarajane Marques Peres |
| Abstract | |
Recently, it has been noted an increasing interest from computer science for research on gesture analysis. Some of these researches aims at supporting researchers from \"gesture studies\", which studies the use of several body parts for communicative purposes. Researchers of \"gesture studies\" analyze gestures from transcriptions of conversations and discourses recorded in video. For gesture transcriptions, gesture unit segmentation and gesture phase segmentation are usually employed. This study aims to develop strategies for automated segmentation of gestural units and phases of gestures contained in a video in the context of storytelling, formulating the problem as a supervised classification task. Support Vector Machines were selected as classification method, because of its ability to generalize and good results obtained for many complex problems. Support Vector Machines, however, do not consider the temporal aspects of data, characteristics that are important for gesture analysis. Therefore, this paper investigates methods of temporal representation and variations of the Support Vector machines that consider temporal reasoning. Several experiments were performed in this context for gesture units segmentation. The best results were obtained with traditional Support Vector Machines applied to windowed data. In addition, three strategies of multiclass classification were applied to the problem of gesture phase segmentation. The results indicate that a good performance for gesture segmentation can be obtained by training the strategy with an initial part of the video to get an automated segmentation of the rest of the video. Thus, researchers in \"gesture studies\" could manually segment only part of the video, reducing the time needed to perform the analysis of gestures contained in long recordings. (AU) | |
| FAPESP's process: | 11/04608-8 - Kernel-based learning algorithms applied to human behaviour analysis: exploring dynamic patterns |
| Grantee: | Renata Cristina Barros Madeo |
| Support Opportunities: | Scholarships in Brazil - Master |