A saliency-based approach to boost trail detection

Abstract

This paper presents a saliency-based solution to boost trail detection. The proposed model builds on the empirical observation that trails are usually conspicuous structures in natural environments. This hypothesis is confirmed by the experimental results, where a strong positive correlation between trail location and visual saliency has been observed. These results are due in part to the proposed extensions to a well known visual saliency computational model. This paper goes further and shows that, with a proper analysis of the saliency information alone, the ambiguity regarding both trail's position and approximate skeleton is reduced to three hypotheses in 98% of the tested natural images. This analysis is performed by a set of agents inhabiting the saliency and feature specific intermediate maps. These agents' behaviours exploit implicit, top-down knowledge about the object being sought in an active way. With the proposed model, computationally demanding accurate trail detectors are able to focus their activity in a fraction of the input image, thus promoting robustness and real-time performance.