A motion-based compression algorithm for resource-constrained video camera traps

TL;DR

This paper presents EcoMotionZip, a novel motion-based video compression algorithm tailored for resource-limited camera traps, significantly reducing data size while preserving critical animal motion information for ecological studies.

Contribution

The paper introduces a new motion analysis-based compression method specifically designed for field animal monitoring, addressing unique challenges of edge computing devices.

Findings

Reduces data size by 87% on average across datasets.

Preserves critical insect behaviour information for analysis.

Enhances applicability of low-powered devices for remote animal monitoring.

Abstract

Field-captured video facilitates detailed studies of spatio-temporal aspects of animal locomotion, decision-making and environmental interactions including predator-prey relationships and habitat utilisation. But even though data capture is cheap with mass-produced hardware, storage, processing and transmission overheads provide a hurdle to acquisition of high resolution video from field-situated edge computing devices. Efficient compression algorithms are therefore essential if monitoring is to be conducted on single-board computers in situations where such hurdles must be overcome. Animal motion tracking in the field has unique characteristics that necessitate the use of novel video compression techniques, which may be underexplored or unsuitable in other contexts. In this article, we therefore introduce a new motion analysis-based video compression algorithm specifically designed for camera traps. We implemented and tested this algorithm using a case study of insect-pollinator motion tracking on three popular edge computing platforms. The algorithm identifies and stores only image regions depicting motion relevant to pollination monitoring, reducing overall data size by an average of 87% across diverse test datasets. Our experiments demonstrate the algorithm's capability to preserve critical information for insect behaviour analysis through both manual observation and automatic analysis of the compressed footage. The method presented in this paper enhances the applicability of low-powered computer vision edge devices to remote, in situ animal motion monitoring, and improves the efficiency of playback during behavioural analyses. Our new software, EcoMotionZip, is available Open Access.