Publicação
Visual pedestrian detection using integral channels for ADAS
| Resumo: | This work exploits a vision based pedestrian recognition technique to build a framework capable of performing detection in images of urban setting. This method takes advantage of the richness of information present in multiple channels of an image to assemble different detection techniques in a simple and generic infrastructure. The general idea behind this method is that pedestrians present specifc visual properties that can be used to differentiate them apart from a scene. So, to exploit such properties, features are extracted from the channels in an optimized manner through the use of integral images and a machine learning engine based on AdaBoost is trained through positive and negative examples the relationship of those features with the presence of pedestrians on visual data. This framework then integrates a multi-scale, sliding window approach to perform visual pedestrian detection. To evaluate the algorithm, tests were carried out in two distinct datasets and results confirm its validity. |
|---|---|
| Autores principais: | Silva, Pedro Batista e |
| Assunto: | Engenharia mecânica Peões - Detecção remota Tráfego urbano |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| Resumo: | This work exploits a vision based pedestrian recognition technique to build a framework capable of performing detection in images of urban setting. This method takes advantage of the richness of information present in multiple channels of an image to assemble different detection techniques in a simple and generic infrastructure. The general idea behind this method is that pedestrians present specifc visual properties that can be used to differentiate them apart from a scene. So, to exploit such properties, features are extracted from the channels in an optimized manner through the use of integral images and a machine learning engine based on AdaBoost is trained through positive and negative examples the relationship of those features with the presence of pedestrians on visual data. This framework then integrates a multi-scale, sliding window approach to perform visual pedestrian detection. To evaluate the algorithm, tests were carried out in two distinct datasets and results confirm its validity. |
|---|