A human-computer collaborative workflow for the acquisition and analysis of terrestrial insect movement in behavioral field studies

Khairi Reda,Victor Mateevitsi,Catherine Offord

doi:10.1186/1687-5281-2013-48

Abstract

The study of insect behavior from video sequences poses many challenges. Despite the advances in image processing techniques, the current generation of insect tracking tools is only effective in controlled lab environments and under ideal lighting conditions. Very few tools are capable of tracking insects in outdoor environments where the insects normally operate. Furthermore, the majority of tools focus on the first stage of the analysis workflow, namely the acquisition of movement trajectories from video sequences. Far less effort has gone into developing specialized techniques to characterize insect movement patterns once acquired from videos. In this paper, we present a human-computer collaborative workflow for the acquisition and analysis of insect behavior from field-recorded videos. We employ a human-guided video processing method to identify and track insects from noisy videos with dynamic lighting conditions and unpredictable visual scenes, improving tracking precision by 20% to 44% compared to traditional automated methods. The workflow also incorporates a novel visualization tool for the large-scale exploratory analysis of insect trajectories. We also provide a number of quantitative methods for statistical hypothesis testing. Together, the various components of the workflow provide end-to-end quantitative and qualitative methods for the study of insect behavior from field-recorded videos. We demonstrate the effectiveness of the proposed workflow with a field study on the navigational strategies of Kenyan seed harvester ants.

Highlights

Characterizing and understanding insect movement patterns is a challenging endeavor
Despite the advances in image processing techniques, the current generation of insect tracking tools is only effective in controlled lab environments and under ideal lighting conditions
We presented an end-to-end workflow for the acquisition, processing, and analysis of the movement trajectories of terrestrial insects in the field

Summary

Introduction

Characterizing and understanding insect movement patterns is a challenging endeavor. Due to the stochastic nature of insect motion, researchers often need to analyze large trajectory datasets that capture their movement under diverse conditions to accurately interpret their behavior. Seed harvester ants live in large colonies of many thousands of individuals and create enormous, persistent networks of trails to guide foragers to food sources up to 40 m from the nest. Many ants leave these trails to search for seeds individually [33]. Rather than tracking all ants on the trail, we opted to obtain few high-quality samples that represent the typical behavior of foragers along the trail This was done by having a human analyst click on a target ant, with the program performing image processing and tracking and prompting the user for input when ambiguities occur. We report on the reliability and accuracy of our human-guided video processing technique and compare it against a fully automated solution in the following

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Conclusion