Shape-guided superpixel grouping for trail detection and tracking

We describe a framework for detecting and tracking continuous ldquotrailsrdquo in images and image sequences for autonomous robot navigation. Continuous trails are extended regions along the ground such as roads, hiking paths, rivers, and pipelines which can be navigationally useful for ground-based or aerial robots. Our approach to single-image trail segmentation incorporates both bottom-up and top-down processes. First, good grouping hypotheses are efficiently generated by probabilistic clustering of superpixels based on color similarity. Second, hypotheses are robustly ranked with an objective function comprising shape, appearance, and deformation terms. The shape term measures how well a triangle, the approximate template for a trail viewed under perspective, can be fit to the groupingpsilas boundary. The appearance term reflects the visual contrast between the grouping and its surroundings using a between-class/within-class scatter measure. Finally, the deformation term measures the closeness of the fitted triangle to a learned distribution which captures expected size, location, and other degrees of shape variation. Although trail detection is accurate and reasonably fast on a variety of isolated images, we describe how introducing temporal filtering to both the bottom-up and top-down stages increases segmentation accuracy and per-frame speed over image sequences. Results are shown on varied sequences collected from flying and driving platforms, as well as images sampled from the Web.