Abstract
We present a biologically inspired long-term object tracking system 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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