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Detection of continuous barking actions from search and rescue dogs' activities data

机译：从搜救犬的活动数据中检测连续的吠叫动作

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We have developed a method to visualize search and rescue (SAR) dogs' activities from sensor data recorded by the SAR dogs' sensor vests. This paper proposes two methods for detecting continuous barking actions of SAR dogs, which locate victims by smell and then bark continuously to tell handlers where victims are located. Continuous barking action is detected from audio information and a dog's body motions. This detection method is based on dynamic time warping (DTW), which has been used successfully to analyze human audio information. Cyclic body motion was observed during dogs' barking motions. This cyclic motion can be detected by an inertial measurement unit (IMU) attached to the vest. A fast Fourier transform (FFT) is used to analyze a dog's barking motion. The proposed detection methods were evaluated using audio and IMU data recorded during actual SAR dog training sessions. The F-scores of the audio and motion-based barking detection methods were 0.95 and 0.90, respectively. As a trial, we marked victim locations on a map based on the body motion.
机译：我们已经开发出一种方法，可以从SAR狗的传感器背心记录的传感器数据中可视化搜救（SAR）狗的活动。本文提出了两种检测SAR犬连续吠叫行为的方法，即通过嗅觉定位受害人，然后连续吠叫以告知处理人员受害人的位置。从音频信息和狗的身体动作中检测到​​连续的吠叫动作。这种检测方法基于动态时间规整（DTW），已成功地用于分析人类音频信息。在狗的吠叫运动中观察到了周期性的身体运动。可以通过安装在背心上的惯性测量单元（IMU）来检测这种循环运动。快速傅立叶变换（FFT）用于分析狗的吠叫运动。建议的检测方法是使用在实际SAR狗训练期间记录的音频和IMU数据进行评估的。音频和基于运动的吠叫检测方法的F分数分别为0.95和0.90。作为试验，我们根据人体运动在地图上标记了受害者的位置。

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