Visual Person Tracking Using a Cognitive Observation Model
作者： Simone Frintrop , Dirk Schulz , Frank Hoeller
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摘要: onigs 2 Abstract— In this article we present a cognitive approach to person tracking from mobile platform. The core of the technique is biologically inspired observation model that combines several feature channels in an object and background dependent way, order optimally separate background. This can be learned quickly single training image easily adaptable different objects. We show how integrated into visual tracker based on well known Condensation algorithm. Several experiments carried out with robot office environment illustrate advantage compared Camshift algorithm which relies fixed features for tracking.
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