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. Insect behaviour has become an important research direction in the field of plant protection 1 . Behavioural research may inform methods for biological control 2,3 , biological model construction 4,5 and plant-insect interactions 6, 7 . To study the behaviour of flying insects accurately, it is necessary to observe and record their flight trajectory quantitatively and precisely in three dimensions (3D). Traditional detection methods for insect behaviour depend mainly on direct and manual observation, which are complicated by arbitrary qualification, wasting of human resources and low effectiveness 8 . Recent developments in computer vision have stimulated the application of these techniques to insect tracking [9] [10] [11] . Straw et al. 12 used three cameras to three-dimensionally track flying animals and can also track flies and birds, but the use of three cameras increased the difficulty of matching and the amount of 3D coordinate calculations. Okubo et al. 13 obtained images of a group of mosquitoes with a single camera and constructed the 3D trajectory of mosquitoes based on the geometric relationship between the mosquito group and its shadow on a white background. Stowers et al. 14 used the FreemoVR platform to establish a height-aversion assay in mice and studied visuomotor effects in Drosophila and zebrafish. However, 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. However, in these studies, the experimental environment was relatively simple, and the target was obvious. Automated image-based tracking has been applied for outdoor research, and the imaging method includes thermal infrared , and harmonic radar 20 methods. Xu et al. 21 proposed a method for the 3D observation of fish based on a single camera. A waterproof mirror was installed above an experimental fish tank to simulate a camera shooting from top to bottom. Although monocular vision was able to determine a 3D trajectory, the shooting video was largely influenced by environmental factors, and the calculation process was complex. Hardie and Powell 22 investigated the use of two or more parallel cameras to obtain an image sequence
Yang et al. REF present a method to analyze frames extracted from videos using kernelized correlation filters (KCF) and background subtraction (BS) (KCF-BS) to plot the 3D trajectory of cabbage butterfly.
Target tracking and 3D trajectory acquisition of cabbage butterfly (P. rapae) based on the KCF-BS algorithm
Scientific Reports
Scientific Reports
Computer Science Medicine