Name: Mariella Berger Andrade
Type: PhD thesis
Publication date: 09/04/2015
Advisor:

Namesort descending Role
Alberto Ferreira De Souza Advisor *
Thiago Oliveira dos Santos Co-advisor *

Examining board:

Namesort descending Role
Alberto Ferreira De Souza Advisor *
Claudine Santos Badue Gonçalves Internal Examiner *
Edilson de Aguiar Internal Examiner *
Evandro Ottoni Teatini Salles External Examiner *
Felipe Maia Galvão França External Examiner *
Thiago Oliveira dos Santos Co advisor *

Summary: Visual search is the mechanism that involves a scan of the visual field in order to find an object of interest. The brain region responsible for performing the visual search, performed by saccadic eye movements, is the Superior Colliculus. A computer system for visual search biologically inspired needs to modell the saccadic eye movement, the transformation suffered by the images captured by the eyes in the way from the retina to the Superior Colliculus, and the response of the neurons of the Superior Colliculus to patterns of interest in the visual scene.
In this work, we present a biologically inspired long-term object tracking ystem based on Virtual Generalizing Random Access Memory (VG-RAM) Weightless Neural Networks (WNN). VG-RAM WNN is an effective machine learning technique that offers simple implementation and fast training. Our system models the biological saccadic eye movement, the transformation suffered by the images captured by the eyes from the retina to the Superior Colliculus (SC), and the response of SC neurons to previously seen patterns. We evaluated the performance of our system using a well-known visual tracking database. Our experimental results show that our approach is capable of reliably and efficiently track an object of interest in a video with accuracy equivalent or superior to related work.

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