| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000560417 |
| ISSN: | 1405-5546 |
| Autors: | Ruelas, Israel1 Torres Blanco, Gustavo1 Ortega Cisneros, Susana3 Moya Sánchez, E. Ulises2 |
| Institucions: | 1Universidad Autónoma de Guadalajara, Departamento de Ciencias Computacionales, Guadalajara, Jalisco. México 2Barcelona Supercomputing Center, Barcelona. España 3Instituto Politécnico Nacional, Centro de Investigación y de Estudios Avanzados, Zapopan, Jalisco. México |
| Any: | 2018 |
| Període: | Oct-Dic |
| Volum: | 22 |
| Número: | 4 |
| Paginació: | 1077-1083 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Resumen en inglés | Neuromorphic sensors such as the Dynamic Vision Sensor (DVS) emulate the behavior of the primary vision system. Its asynchronous behavior makes the data processing easier and faster due to the analysis is only in the active pixels. Pedestrian kinematics contains specific movement patterns feasible to be detected, like the angular movement of arms and feet. Some previous methodologies were focused on pedestrian detection based on the static shapes detection like cylinders or circles, however, they do not take into account the kinematic behavior of the body by itself. In this paper, we presented an algorithm inspired in K-means clustering and describes the analysis of the human kinematics based on DVS in order to detect and track pedestrians in a controlled environment. |
| Disciplines | Ciencias de la computación |
| Paraules clau: | Inteligencia artificial |
| Keyword: | Dynamic vision sensor, Pedestrian detection, Pedestrian tracking, Artificial intelligence |
| Text complet: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
