Investigating the Effectiveness of 3D Monocular Object Detection Methods for Roadside Scenarios

Urban environments are demanding effective and efficient detection in 3D of objects using monocular cameras, e.g., for intelligent monitoring or decision support. The limited availability of large-scale roadside camera datasets and the mere focus of existing 3D object detection methods on autonomous driving scenarios pose significant challenges for their practical adoption, unfortunately. In this paper, we conduct a systematic analysis of 3D object detection methods, originally applied to autonomous driving scenarios, on monocular roadside images. Under a common evaluation protocol, based on a synthetic dataset with images from monocular roadside cameras located at intersection areas, we analyzed the detection quality achieved by these methods in the roadside context and the influence of key operational parameters. Our study finally highlights open challenges and future directions in this field.