EWFootstep 1.0

Due to the limitations of visual surveillance systems, such as obtrusiveness and high power requirements, audio-based surveillance has gained significant traction in security applications. Among these, footstep-based audio analysis has emerged as a promising and non-intrusive approach for monitoring and threat detection. EWFootstep 1.0, a novel dataset comprising recordings from 176 subjects collected under real-world environmental conditions, distinguishing footstep acoustic signatures of single and multiple persons across forests, roads, and indoor settings. To validate the dataset, we perform time & frequency domain analyses, and implement a CNN-based baseline model. Frechet Audio Distance (FAD) and t-SNE visualizations are carried out to evaluate audio similarity and feature separability across different classes. The dataset bridges the gap in footstep-based security and forensic research by providing a comprehensive dataset for machine learning applications.