Tree trunk detection using contrast templates

We propose a simple contrast-based method for tree detection and shape estimation from ground-plane perspective images for purposes of counting, classification, modeling, or robotic obstacle avoidance. Under the assumption that tree trunks are relatively narrow and vertical shapes which strongly differ in appearance from the scene background and have boundaries of opposite contrast, we apply a bank of bar filters parametrized from camera intrinsics combined with trunk location and diameter limits, and integrate results vertically. Non-maximum suppression is applied to candidates in the resulting trunk likelihood image. We present results demonstrating the effectiveness of our tree detection algorithm on a variety of forested images obtained directly from our robot as well as sampled from the web, and quantify performance using ground truth on a subset of those images. We also compare the results of our method with several related published approaches.