Bird monitoring using the smartphone (iOS) application <i>Videography</i> for motion detection

<b>Capsule:</b> Automated monitoring of daily activity in birds can be facilitated by the use of a smartphone with video analysis software. <b>Aims:</b> To test the application of an iPhone 5 smartphone with video motion detection for monitoring birds remotely, transferring the data over a mobile network and extracting the data automatically. <b>Methods:</b> A customized bird feeder was used at an established feeding site where high visitor frequency provided a rigorous test of procedures. The application <i>Videography</i> was used to detect birds visiting the feeder. This runs advanced algorithms on the camera input to detect motion and trigger automated video recordings. <b>Results:</b> Recorded video was stored immediately in a cloud service or locally on the smartphone. The data were automatically processed using R-scripts, obviating the need for manual data entry prior to analysis. Bird visits to the feeder were distributed throughout the day, increasing rapidly after sunrise and ceasing before dusk. <b>Conclusions:</b> The system offered a novel and efficient means of automatically monitoring birds visiting a central place, and it would be suitable for close-up monitoring of birds that regularly visit the entrances of cavities, nests or feeding places.**研究概要**：借助搭载视频分析软件的智能手机，可实现鸟类日常活动的自动化监测。 **研究目标**：测试搭载视频运动检测功能的iPhone 5智能手机在远程监测鸟类、通过移动网络传输数据以及自动提取数据方面的应用效果。 **研究方法**：在一处访客频次较高的既定投喂点使用定制鸟类投喂器，以对整套流程开展严格测试。使用名为《Videography》的软件对到访投喂器的鸟类进行检测，该软件可对摄像头采集的画面运行高级算法，以检测运动并触发自动录像。 **研究结果**：录制的视频会即刻存储至云服务或智能手机本地。通过R脚本自动处理数据，无需在分析前手动录入数据。鸟类到访投喂器的时段分布于全天，日出后迅速增多，于黄昏前停止。 **研究结论**：本系统为自动监测到访固定点位的鸟类提供了一种新颖且高效的手段，适用于对定期到访树洞、鸟巢或投喂点入口的鸟类开展近距离监测。

搜集汇总

背景与挑战

背景概述

该数据集基于智能手机应用Videography进行鸟类运动检测的监测研究，通过iPhone 5和定制喂食器实现远程自动化数据采集与处理，避免了手动操作。数据集包含视频、R脚本和文档文件，适用于鸟类行为分析和生态监测，发布于2017年并采用CC BY 4.0许可。