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<p>Due to the limitations of visual surveillance systems, such as line-of-sight, obtrusiveness, and high power requirements, audio-based surveillance has gained significant traction in security and forensic applications. Among these, footstep-based audio analysis has emerged as a promising and non-intrusive approach for monitoring and threat detection. This paper introduces 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 individuals across forests, roads, and indoor settings. To validate the dataset, we perform time and frequency domain analyses, and implement machine learning (ML) and convolutional neural network (CNN) based baseline models. Feature separability is visualized using t-SNE and quantified using Davies-Bouldin Index. Our work bridges the gap in footstep-based security by providing a comprehensive dataset for machine learning applications in surveillance and crime scene investigation.</p>

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EWFootstep 1.0: footstep data for early warning and surveillance systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

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  • Anshuman Agrahri,
  • Chandresh Kumar Maurya,
  • Ravi Shekhar Tiwari,
  • Shashwat Tripathi

摘要

Due to the limitations of visual surveillance systems, such as line-of-sight, obtrusiveness, and high power requirements, audio-based surveillance has gained significant traction in security and forensic applications. Among these, footstep-based audio analysis has emerged as a promising and non-intrusive approach for monitoring and threat detection. This paper introduces 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 individuals across forests, roads, and indoor settings. To validate the dataset, we perform time and frequency domain analyses, and implement machine learning (ML) and convolutional neural network (CNN) based baseline models. Feature separability is visualized using t-SNE and quantified using Davies-Bouldin Index. Our work bridges the gap in footstep-based security by providing a comprehensive dataset for machine learning applications in surveillance and crime scene investigation.