Fig 1.
Experimental setup and trajectory estimation methodology.
(a) The experimental setup consisted of a plexiglass cage, fabric net, sugar water bottle, mosquitoes, and camera. The number of mosquitoes in each recording was different. (b) The trajectory estimation was based on the mask RCNN framework and cubic spline interpolation. The training images data was fed into the Mask RCNN framework. Mask RCNN consists of RoIAlign to preserve spatial information. RoIAlign uses binary interpolation, which creates fix size feature map. RoIAlign layer output is fed into the mask head, which is consisted of two convolutional layers. Through this, masks are generated for each ROI, thus pixel to pixel segmentation of the images. Then video sequence data were processed using the trained model, and coordinates were extracted. Finally, the cubic spline interpolation was applied to fill the missing data smoothly.
Fig 2.
Impact of interpolation on trajectory estimation.
In the top 2 charts, the impact of interpolation with and without interpolation is shown individually, while in the bottom graphs, they are shown along with ground truth trajectories. It can be seen from the green dotted circled areas that spline interpolation helped to fill the missing points and achieve continuous tracking with higher accuracy.
Fig 3.
Scenario 1 results obtained from Mask RCNN and interpolation.
Flying mosquitoes’ flight starting points are shown with dots, while the flight endpoints are shown with arrows. The mosquitoes in the ‘rest’ position are presented with filled marker dots. To distinguish the ground truth trajectories from estimated trajectories which will be discussed in the next section, the names of the mosquitoes for estimated trajectories are indicated with asterisk symbols. Different colours are also used to distinguish the mosquitoes from each other.
Fig 4.
Scenario 2 results obtained from Mask RCNN and interpolation.
Fig 5.
Scenario 1 results obtained from Mask RCNN and interpolation.
Fig 6.
Comparison of estimated and ground-truth values for scenario 1.
Table 1.
Performance evaluation metric scenario 1.
Fig 7.
Comparison of estimated and ground-truth values for scenario 2.
Table 2.
Performance evaluation metric scenario 2.
Fig 8.
Comparison of estimated and ground-truth values for scenario 3.
Table 3.
Performance evaluation metric scenario 3.
Fig 9.
Error tracking for scenario 1.
Fig 10.
Error tracking for scenario 2.
Fig 11.
Error tracking for scenario 3.