Single camera 3D perception for traffic monitoring faces significant challenges due to occlusion
and
limited field of view. Moreover, fusing information from multiple cameras at the image feature level is
difficult because of different view angles. Further, the necessity for practical implementation and
compatibility with existing traffic infrastructure compounds these challenges. To address these issues,
this paper introduces a novel Bird's-Eye-View road occupancy detection framework that leverages multiple
roadside cameras to overcome the aforementioned limitations. To facilitate the framework's development and
evaluation, a synthetic dataset featuring diverse scenes and varying camera configurations is generated
using the CARLA simulator. A late fusion and three early fusion methods were implemented within the
proposed framework, with performance further enhanced by integrating backgrounds. Extensive evaluations
were conducted to analyze the impact of multi-camera inputs and varying BEV occupancy map sizes on model
performance. Additionally, a real-world data collection pipeline was developed to assess the model's
ability to generalize to real-world environments. The sim-to-real capabilities of the model were evaluated
using zero-shot and few-shot fine-tuning, demonstrating its potential for practical application. This
research aims to advance perception systems in traffic monitoring, contributing to improved traffic
management, operational efficiency, and road safety.
