Pedestrian tracking using probability fields and a movement feature space1
Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other moving objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity, modeling its trajecto...
- Autores:
-
Negri, Pablo
Garayalde, Damian
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2017
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/60446
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/60446
http://bdigital.unal.edu.co/58778/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
pedestrian tracking
movement feature space
target framework
seguimiento de peatones
espacio de descriptores dinámicos
target framework
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other moving objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity, modeling its trajectory and speed by using a state machine. The particularity of our methodology is the use of a Movement Feature Space (MFS) to generate descriptors for classifiers and trackers. This approach is applied to two public sequences (PETS2009 and TownCentre). The results of this tracking outperform other algorithms reported in the literature, which have, however, a higher computational complexity. |
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