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Title: Neural-Swarm Visual Saliency for Path Following
Authors: Santana, P.
Mendonça, R.
Correia, L.
Barata, J.
Keywords: Swarm cognition
Swarm intelligence
Neural-swarm models
Visual saliency
Path detection and tracking
Autonomous robots
Issue Date: Jun-2013
Publisher: Elsevier
Abstract: This paper extends an existing saliency-based model for path detection and tracking so that the appear- ance of the path being followed can be learned and used to bias the saliency computation process. The goal is to reduce ambiguities in the presence of strong distractors. In both original and extended path detectors, neural and swarm models are layered in order to attain a hybrid solution. With generalisation to other tasks in mind, these detectors are presented as instances of a generic neural-swarm layered architecture for visual saliency computation. The architecture considers a swarm-based substrate for the extraction of high-level perceptual representations, given the low-level perceptual representations extracted by a neural-based substrate. The goal of this division of labour is to ensure parallelism across the vision system while maintaining scalability and tractability. The proposed model is shown to exhibit, at 20 Hz, a 98.67% success rate on a diverse data-set composed of 39 videos encompassing a total of 29,789 640 × 480 frames. An open source implementation of the model, fully encapsulated as a node of the Robotics Operating System (ROS), is available for download
Description: WOS:000319205600003 (Nº de Acesso Web of Science)
Peer reviewed: Sim
ISSN: 1568-4946
Publisher version: The definitive version is available at:
Appears in Collections:CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica
IT-RI - Artigo em revista internacional com arbitragem científica

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