Radar-Based People Counting Using ML
Jan 2026
Machine learning pipeline for radar-based people counting in noisy real-world environments and constrained deployment settings.
This project explores radar-based people counting with a focus on noisy real-world environments. The pipeline combines signal processing, feature extraction, and machine learning models while keeping deployment constraints in mind.
Key emphasis areas include reducing clutter, improving count accuracy, and building a workflow that remains practical for resource-constrained systems.