Publicação
Infrastructure for machine learning and computer vision
| Resumo: | The infrastructure surrounding machine learning projects is of utmost importance: Machine learning projects require data acquisition mechanisms, software for data processing, as well as a benchmarking platform for evaluating performance of machine learning algorithms over time. In this report we describe our work aimed at developing such infrastructure for a Europe based computer vision startup specializing in human behaviour tracking. We discuss three projects comprising the work. One dedicated to creating a machine learning dataset for human behaviour monitoring, another to developing a screen-camera calibration tool, and third to setting up a benchmarking platform. The projects were integrated with the core technology of the startup, and will continue to be applied in the future. |
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| Autores principais: | Chikhladze, Dimitri |
| Assunto: | Data acquisition mechanisms Data processing Benchmarking platform Human behaviour monitoring |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The infrastructure surrounding machine learning projects is of utmost importance: Machine learning projects require data acquisition mechanisms, software for data processing, as well as a benchmarking platform for evaluating performance of machine learning algorithms over time. In this report we describe our work aimed at developing such infrastructure for a Europe based computer vision startup specializing in human behaviour tracking. We discuss three projects comprising the work. One dedicated to creating a machine learning dataset for human behaviour monitoring, another to developing a screen-camera calibration tool, and third to setting up a benchmarking platform. The projects were integrated with the core technology of the startup, and will continue to be applied in the future. |
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