Superpixel analysis for object detection and tracking with application to UAV imagery

Abstract

We introduce a framework for object detection and tracking in video of natural outdoor scenes based on fast per-frame segmentations using Felzenszwalb's graph-based algorithm. Region boundaries obtained at multiple scales are first temporally filtered to detect stable structures to be considered as object hypotheses. Depending on object type, these are then classified using o priori appearance characteristics such as color and texture and geometric attributes derived from the Hough transform. We describe preliminary results on image sequences taken from low-flying aircraft in which object categories are relevant to UAVs, consisting of sky, ground, and navigationally-useful ground features such as roads and pipelines. © Springer-Verlag Berlin Heidelberg 2007.