Segmentation and Recognition of Highway Assets Using Image-Based 3D Point Clouds and Semantic Texton Forests

Abstract

AbstractEfficient data collection of high-quantity and low-cost highway assets such as road signs, traffic signals, light poles, and guardrails is a critical element to the operation, maintenance, and preservation of transportation infrastructure systems. Despite its importance, current practice of highway asset data collection is time-consuming, subjective, and potentially unsafe. The high volume of the data that needs to be collected can also negatively impact the quality of the analysis. To address these limitations, this paper proposes a new algorithm for semantic segmentation and recognition of highway assets using video frames collected from a car-mounted camera. The proposed set of algorithms (1) takes the captured frames and using a pipeline of structure from motion and multiview stereo reconstructs a three-dimensional (3D) point cloud model of the highway and surrounding assets; (2) using a Semantic Texton Forest classifier, each geo-registered two-dimensional (2D) video frame at the pixel-level ...