Abstract
Insect behaviour is an important research topic in plant protection. To study insect behaviour accurately, it is necessary to observe and record their flight trajectory quantitatively and precisely in three dimensions (3D). The goal of this research was to analyse frames extracted from videos using Kernelized Correlation Filters (KCF) and Background Subtraction (BS) (KCF-BS) to plot the 3D trajectory of cabbage butterfly (P. rapae). Considering the experimental environment with a wind tunnel, a quadrature binocular vision insect video capture system was designed and applied in this study. The KCF-BS algorithm was used to track the butterfly in video frames and obtain coordinates of the target centroid in two videos. Finally the 3D trajectory was calculated according to the matching relationship in the corresponding frames of two angles in the video. To verify the validity of the KCF-BS algorithm, Compressive Tracking (CT) and Spatio-Temporal Context Learning (STC) algorithms were performed. The results revealed that the KCF-BS tracking algorithm performed more favourably than CT and STC in terms of accuracy and robustness.
Highlights
Insect behaviour has become an important research direction in the field of plant protection[1]
We propose a method for tracking and obtaining 3D trajectories based on a combination of the kernelized correlation filter (KCF) algorithm and background subtraction (BS) (KCF-BS)
To optimize the kernel functions and the characteristics of tracking of cabbage butterfly targets, a top-view video of flight in the wind tunnel was employed as a test
Summary
Insect behaviour has become an important research direction in the field of plant protection[1]. Stowers et al.[14] used the FreemoVR platform to establish a height-aversion assay in mice and studied visuomotor effects in Drosophila and zebrafish This method was not directly suitable for the investigation of behaviours for which stereopsis is important because it rendered visual stimuli in a perspective-correct manner for a single viewpoint. Jantzen and Eisner[15] implemented Lepidoptera’s 3D trajectory tracking, and Lihoreau et al.[16] obtained the three dimensional foraging flights of bumblebees In these studies, the experimental environment was relatively simple, and the target was obvious. We propose a method for tracking and obtaining 3D trajectories based on a combination of the kernelized correlation filter (KCF) algorithm and background subtraction (BS) (KCF-BS). The proposed method will provide a theoretical foundation and valuable reference for further behavioural research on this insect and a technical reference for target tracking based on orthogonal binocular vision
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