Publicação
Intelligent Techniques for Interactive Video Content
| Resumo: | Video can be used in many contexts, such as capturing important events, entertainment, or producing educational content. Moreover, video annotation and interactive video enable the viewer to actively engage with the video by providing various annotations or interactive elements, such as clickable buttons, hotspots, text, sketches, or other interactive features that enhance the viewing experience. This is especially relevant in contexts where users need to analyze specific types of video footage and highlight moments of interest. However, those highlights may not be easy to identify or interpret, often requiring significant time and expertise, and can lead to errors or overlooked information. Annotation systems can contribute to video analysis and understanding. These annotations enable the identification of significant elements in videos that require interpretation and creativity. These elements can serve as raw material for creating more detailed analyses, storytelling, or educational tools. Furthermore, these systems can facilitate collaboration, as multiple users can contribute annotations and insights, fostering a richer understanding of the video content. This work began with the development of a video annotation tool, which was subsequently refined and enhanced through requirements gathered in interaction with dance, sports and craft experts. This interaction happened partially in the scope of several European projects. These projects provided the necessary evaluation testbeds for the work described in this dissertation. The research focused on developing technologies for dance analysis. Several challenges were identified during these projects, including the need for automatic video content analysis and annotation of video footage. Addressing these challenges sets the research environment for the work described in this document. A new video annotation tool prototype, MotionNotes, was designed and developed to study integration possibilities. It incorporates intelligent techniques for movement analysis using video, employing automatic video annotation to efficiently identify and tag key actions or events within the footage. This situation represents a larger set of possibilities where machine learning can improve interaction and user experience in interactive tools. The prototype underwent an iterative development process characterized by con- tinuous improvement. User participation was instrumental to the evolution of this work, contributing significantly in each iteration to improve existing functionalities and introducing new features, such as using their own video content to discover movement patterns. After extensive evaluations, MotionNotes proved to be a success- ful instance of an intelligent video annotation tool, enabling techniques that make it possible to augment video information. Therefore, the insights and findings outlined in this document resulted from our exploration of interactive video annotation, com- bined with machine learning, as a representative of a larger class of systems where intelligent tools can help human tasks. |
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| Autores principais: | Rodrigues, Rui Filipe dos Santos |
| Assunto: | Video Augmentation and Annotation Intelligent Tools Human Pose Estimation Movement Detection Multimodal Data Creative Processes |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | Video can be used in many contexts, such as capturing important events, entertainment, or producing educational content. Moreover, video annotation and interactive video enable the viewer to actively engage with the video by providing various annotations or interactive elements, such as clickable buttons, hotspots, text, sketches, or other interactive features that enhance the viewing experience. This is especially relevant in contexts where users need to analyze specific types of video footage and highlight moments of interest. However, those highlights may not be easy to identify or interpret, often requiring significant time and expertise, and can lead to errors or overlooked information. Annotation systems can contribute to video analysis and understanding. These annotations enable the identification of significant elements in videos that require interpretation and creativity. These elements can serve as raw material for creating more detailed analyses, storytelling, or educational tools. Furthermore, these systems can facilitate collaboration, as multiple users can contribute annotations and insights, fostering a richer understanding of the video content. This work began with the development of a video annotation tool, which was subsequently refined and enhanced through requirements gathered in interaction with dance, sports and craft experts. This interaction happened partially in the scope of several European projects. These projects provided the necessary evaluation testbeds for the work described in this dissertation. The research focused on developing technologies for dance analysis. Several challenges were identified during these projects, including the need for automatic video content analysis and annotation of video footage. Addressing these challenges sets the research environment for the work described in this document. A new video annotation tool prototype, MotionNotes, was designed and developed to study integration possibilities. It incorporates intelligent techniques for movement analysis using video, employing automatic video annotation to efficiently identify and tag key actions or events within the footage. This situation represents a larger set of possibilities where machine learning can improve interaction and user experience in interactive tools. The prototype underwent an iterative development process characterized by con- tinuous improvement. User participation was instrumental to the evolution of this work, contributing significantly in each iteration to improve existing functionalities and introducing new features, such as using their own video content to discover movement patterns. After extensive evaluations, MotionNotes proved to be a success- ful instance of an intelligent video annotation tool, enabling techniques that make it possible to augment video information. Therefore, the insights and findings outlined in this document resulted from our exploration of interactive video annotation, com- bined with machine learning, as a representative of a larger class of systems where intelligent tools can help human tasks. |
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Atividades financiadas
Não foram encontrados projetos financiados.