CINECA IRIS Institutional Research Information System
IRIS è l’archivio della produzione scientifica della Fondazione Bruno Kessler.
EnglishStatistical models for tracking different moving bodies must be able to reason about occlusions in order to be effective. Representing the joint statistics across different bodies is computationally hard, since the size of the representation grows exponentially with the number of bodies being tracked. Separable tracking, with one tracker per body, cannot deal with occlusions effectively. We propose a new model, dubbed Hybrid Joint-Separable (HJS), that uses a representation size that grows linearly with the number of bodies, and a computational complexity that grows quadratically. This model can reason explicitly about occlusions. We describe a particle filter implementation of this model, and present promising experimental results.
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte di FBK.
| Titolo: | Hybrid Joint-Separable Multibody Tracking |
| Autori: | |
| Data di pubblicazione: | 2005 |
| Abstract: | Statistical models for tracking different moving bodies must be able to reason about occlusions in order to be effective. Representing the joint statistics across different bodies is computationally hard, since the size of the representation grows exponentially with the number of bodies being tracked. Separable tracking, with one tracker per body, cannot deal with occlusions effectively. We propose a new model, dubbed Hybrid Joint-Separable (HJS), that uses a representation size that grows linearly with the number of bodies, and a computational complexity that grows quadratically. This model can reason explicitly about occlusions. We describe a particle filter implementation of this model, and present promising experimental results. |
| Handle: | http://hdl.handle.net/11582/11308 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
http://hdl.handle.net/11582/11308
Copyright © 2021