Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset

Abstract

Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects. Finally, we present baseline detection performance results for three leading approaches from urban pedestrian detection and our own convolutional neural network approach that benefits from the incorporation of additional image context. We show that the success of existing approaches on urban data does not transfer directly to this domain.