Real Time Tracking of Wild Animals Using Unmanned Aerial Vehicles Based on YOLOv7-Tiny Algorithm
Utilizing UAV edge computing technology to monitor the movement pattern and population dynamic of wild ani-mals has become a widely adopted technique among researchers.Traditional tracking algorithms demand high computa-tional power,and pose challenges for onboard edge device with limited hardware resources,especially in complex outdoor environment where real-time tracking is difficult to achieve.To address issues such as tree cover and background interfer-ence that hinder accurate real-time tracking during UAV-based wild animals monitoring in outdoor settings,we selected north-east China's wild animals species,including the Amur tiger(Panthera tigris altaica),roe deer(Capreolus pygargus man-tschuricus),and reindeer(Rangifer tarandus phylarchus),as research subjects.Leveraging YOLOv7-Tiny+Bot-SORT as the detection and tracking framework,we proposed a lightweight UAV tracking algorithm.Initially,we employed the Faster-Net network to reduce redundant computations in the model and enhance focus on target regions in feature maps.Subse-quently,an efficient channel attention mechanism facilitated local inter-channel communication to mitigate the impact of com-plex environments on detection networks,thereby improving detection capability.Finally,to lower computational costs,we substituted the re-identification network to enhance UAV tracking speed.Results revealed that the proposed real-time track-ing method achieved accuracy rates of 79.93%for multi-object tracking accuracy(MOTA)and 73.48%for multi-object track-ing precision(MOTP),with tracking speed increasing from 3.4 to 43.4 frames per second.These findings demonstrate not only excellent performance in enhancing tracking accuracy and speed but also suitability for edge devices with limited compu-tational resources,thereby providing robust technical support for biodiversity conservation and population behavioral studies.
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