Robust single target tracking using determinantal point process observations

dc.contributor.author	Hernández, Sergio
dc.contributor.author	Sallis, Philip
dc.contributor.author	Sallis
dc.date.accessioned	2021-11-09T12:42:38Z
dc.date.available	2021-11-09T12:42:38Z
dc.date.issued	2020
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3443
dc.description.abstract	The efficiency and robustness of modern visual tracking systems are largely dependent on the object detection system at hand. Bernoulli and Multi-Bernoulli filters have been proposed for visual tracking without explicit detections (image observations). However, these previous approaches do not fully exploit discriminative features for tracking. In this paper, we propose a novel Bernoulli filter with determinantal point processes observations. The proposed observation model can select groups of detections with high detection scores and low correlation among the observed features; thus achieving a robust filter.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	International Journal on Smart Sensing and Intelligent Systems, 13(1), 1-8	es_CL
dc.subject	Visual tracking	es_CL
dc.subject	Bernoulli filter	es_CL
dc.title	Robust single target tracking using determinantal point process observations	es_CL
dc.type	Article	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.uri	www.exeley.com/in_jour_smart_sensing_and_intelligent_systems/doi/10.21307/ijssis-2020-001	es_CL
dc.ucm.doi	doi.org/10.21307/ijssis-2020-001	es_CL

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