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This paper extends an existing saliency-based model for path detection and tracking so that the appearance 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 genericdoi:10.1016/j.asoc.2012.07.011 fatcat:t7nrfm5phjboxblivjotkwmioq