Poster Abstract: Listen and Then Sense: Vibration-Based Sports Crowd Monitoring by Pre-Training with Public Audio Datasets

This paper addresses challenges in monitoring human behavior in crowds through floor vibration sensing, overcoming limitations like subjective manual observation, visual occlusions, and audio interference. Our approach involves tackling limited-data vibration signal tasks by conducting pre-training across modalities, leveraging publicly available audio datasets. By leveraging self-supervised representation learning to pre-train on publicly available audio datasets, our approach reduces data requirements, improves robustness, and minimizes the need for human labeling efforts. Evaluation using in-game stadium vibration data with YouTube audio dataset demonstrates up to 5.8× error reduction for crowd behavior.