Top-View Noise-Filtered Point Cloud of 2-wheelers, 4-wheelers and Pedestrians

Various modes of transportation traverse our roadways, highlighting the importance of object classification for improving traffic safety. Optical sensors that rely on visual data encounter challenges in adverse weather conditions, where poor visibility hinders target classification. In this project we use an off-the-shelf millimeter wave Frequency Modulated Continuous Wave (FMCW) radar -- Texas Instruments IWR1843BOOST module to classify on road objects. By combining the radar module, Robot Operating System (ROS), and Python scripts, we extracted a dataset of 3D point cloud images. The images were preprocessed to create top-view noise-filtered images, and using Machine Learning (ML) models, they were classified into 2-wheelers, 4-wheelers, and pedestrians. The ML model was trained on a dataset comprising approximately 15,000 images.