Services on Demand
Journal
Article
Indicators
- Cited by SciELO
- Access statistics
Related links
- Cited by Google
- Similars in SciELO
- Similars in Google
Share
DYNA
Print version ISSN 0012-7353
Abstract
NEGRI, Pablo and GARAYALDE, Damián. Pedestrian tracking using probability fields and a movement feature space. Dyna rev.fac.nac.minas [online]. 2017, vol.84, n.200, pp.217-227. ISSN 0012-7353. https://doi.org/10.15446/dyna.v84n200.57028.
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.
Keywords : pedestrian tracking; movement feature space; target framework.