Document
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IEEE International Symposium on Industrial Electronics
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publication venue for: An object-tracking algorithm based on Bayesian-learning
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RDF description of An object-tracking algorithm based on Bayesian-learning - http://orbis.uaslp.mx/vivo/individual/scopus8132
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date: 2026-08-25T14:37:44
publisher: vivo
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Information Content Entity
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An object-tracking algorithm based on Bayesian-learning
type: Continuant
type: Generically Dependent Continuant
date time value: conference paper date 8133
estado: published
related by: authorship_52537
freetext keyword: Automation; Bayesian networks; Chlorine compounds; Electronics industry; Imaging techniques; Industrial electronics; Learning algorithms; Photography; Technical presentations; Video recording; Bayesian; International symposium; Learning processes; Moving objects; Object tracking algorithm; Object-tracking; Optimization methods; People tracking; Probabilistic approaches; Real time videos; Real-time object tracking; Tracking algorithms; Video processing; Video tracking; Video-surveillance; Security systems
most specific type: Conference Paper
most specific type: Document
EI dscopus: 2-s2.0-50049104491
related by: authorship_52538
type: Entity
type: Article
origen: scopus
type: Information Content Entity
has publication venue: IEEE International Symposium on Industrial Electronics
type: Document
abstract: Real-time object tracking is recently becoming very important in many video processing tasks. Applications as video surveillance, robotics, people tracking, etc., need reliable and affordable video tracking tools. Most of current available solutions are, however, computationally intensive and sometimes require expensive video hardware. In this paper, we propose a new object tracking algorithm for real-time video based on a new probabilistic approach that results in a Bayesian-Learning process. This approach infers the trajectory of a moving object by applying a very simple optimization method, which makes the tracking algorithm robust and simple to implement. Experimental results are provided to demonstrate the performance of the proposed tracking algorithm in complex real-time video sequence scenarios. ©2007 IEEE.
related by: authorship_52539
type: Conference Paper
crisid: 8133
artnum: 4374857
page start: 1681
type: Thing
doi: 10.1109/ISIE.2007.4374857
page end: 1686

Conference Paper
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