用地震信号检测大象足音，实现远距离实时识别，缓解人象冲突。
Event Detection and Classification for Long Range Sensing of Elephants Using Seismic Signal
- 提出上下文定制窗口法，精准捕捉大象足音
- 自然环境下检测最远达140米，识别准确率70%以上
- 适合资源受限场景，尤其适用于人象冲突高发区
通过地震信号检测大象是应对人象冲突（HEC）的新兴研究方向。现有方法严重依赖人工分类，难以实现实时应用。本文提出一种面向资源受限设备的分类框架，引入专为大象足音设计的上下文定制窗口法（CCW），并对比了短时平均/长时平均（STA/LTA）方法。在受控条件下最大检测距离达155.6米，在自然环境中达140米。采用径向基函数核支持向量机（SVM-RBF）进行足音分类，在受控环境准确率达99%，自然栖息地73%，冲突高发的人类活动区70%，最具挑战性场景。可解释AI分析表明，零交叉数与动态时间规整（DTW）对齐代价是影响分类的关键因素，主频率在受控条件下作用显著。
原文摘要 · Abstract (English)
Detecting elephants through seismic signals is an emerging research topic aimed at developing solutions for Human-Elephant Conflict (HEC). Despite the promising results, such solutions heavily rely on manual classification of elephant footfalls, which limits their applicability for real-time classification in natural settings. To address this limitation and build on our previous work, this study introduces a classification framework targeting resource-constrained implementations, prioritizing both accuracy and computational efficiency. As part of this framework, a novel event detection technique named Contextually Customized Windowing (CCW), tailored specifically for detecting elephant footfalls, was introduced, and evaluations were conducted by comparing it with the Short-Term Average/Long-Term Average (STA/LTA) method. The yielded results show that the maximum validated detection range was 155.6 m in controlled conditions and 140 m in natural environments. Elephant footfall classification using Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel demonstrated superior performance across multiple settings, achieving an accuracy of 99% in controlled environments, 73% in natural elephant habitats, and 70% in HEC-prone human habitats, the most challenging scenario. Furthermore, feature impact analysis using explainable AI identified the number of Zero Crossings and Dynamic Time Warping (DTW) Alignment Cost as the most influential factors in all experiments, while Predominant Frequency exhibited significant influence in controlled settings.
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