Field-captured video allows for detailed studies of spatiotemporal aspects of animal locomotion, decision-making, and environmental interactions. However, despite the affordability of data capture with mass-produced hardware, storage, processing, and transmission overheads pose a significant hurdle to acquiring high-resolution video from field-deployed camera traps. Therefore, efficient compression algorithms are crucial for monitoring with camera traps that have limited access to power, storage, and bandwidth. In this article, we introduce a new motion analysis-based video compression algorithm designed to run on camera trap devices. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing the overall data size by an average of 84% across a diverse set of test datasets while retaining the information necessary for relevant behavioural analysis. The methods outlined in this paper facilitate the broader application of computer vision-enabled, low-powered camera trap devices for remote, in-situ video-based animal motion monitoring.

翻译：野外捕获的视频使得对动物运动、决策制定与环境交互的时空特性进行详细研究成为可能。然而，尽管通过大规模生产的硬件进行数据采集成本低廉，但存储、处理和传输开销对从野外部署的相机陷阱获取高分辨率视频构成了重大障碍。因此，高效的压缩算法对于在电力、存储和带宽受限的相机陷阱监测中至关重要。本文提出了一种新的基于运动分析的视频压缩算法，专为在相机陷阱设备上运行而设计。我们通过昆虫传粉者运动追踪的案例研究实现并测试了该算法。该算法仅识别并存储描绘与传粉监测相关运动的图像区域，在多样化测试数据集上平均将总体数据大小减少84%，同时保留了相关行为分析所需的信息。本文概述的方法促进了具备计算机视觉功能的低功耗相机陷阱设备在远程、原位视频动物运动监测中的更广泛应用。