Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters

Luo, Wei; Zhao, Yongxiang; Shao, Quanqin; Li, Xiaoliang; Wang, Dongliang; Zhang, Tongzuo; Liu, Fei; Duan, Longfang; He, Yuejun; Wang, Yancang; Zhang, Guoqing; Wang, Xinghui; Zhang, Yu

doi:10.3390/s23083948

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The Deep SORT algorithm was used as the baseline algorithm for the tracker and two improvements in the algorithm were made. Firstly, optical flow for motion estimation [43] was introduced into the scheme to improve the motion prediction accuracy of KF. Secondly, an extended version of the original tracking method, named as low confidence track filtering method, was used to improve the ability of the tracker for handling unreliable detection results, which might occur in the real-world target detection due to the complex environment.…”

Section: Trackermentioning

confidence: 99%

Highly Accurate and Reliable Tracker for UAV-Based Herd Monitoring

Luo¹,

Zhang²,

Shao³

et al. 2023

Preprint

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It is a challenging and meaningful task to carry out drone-based livestock monitoring in high-altitude and cold regions. The purpose of AI is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and DeepSORT algorithms for target detection and tracking, respectively. To address the existing problems of the DeepSORT algorithm, the following two optimizations are made: (1) Optical flow is used to compensate the Kalman filter for improvement of the prediction accuracy; (2) A low-confidence trajectory filtering method is adopted to reduce the influence of unreliable detection on target tracking. In addition, an visual servo controller for the UAV is designed to enable the automated tracking task. Finally, the system is tested using the Tibetan yaks living in the Tibetan Plateau as the tracking targets, and the results reveal the real-time multiple tracking ability and the ideal visual servo effect of the proposed system.

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Section: Trackermentioning

confidence: 99%

Highly Accurate and Reliable Tracker for UAV-Based Herd Monitoring

Luo¹,

Zhang²,

Shao³

et al. 2023

Preprint

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show abstract

“…However, drone-based detection faces some challenges. For example, Luo et al highlighted that when drones are applied to track and monitor dense populations of Proctor antelopes, the presence of individual antelopes obstructing each other can decrease detection accuracy [ 10 ]. Moreover, improving detection accuracy often requires increased hardware configuration and computational resources, posing logistical and financial challenges.…”

Section: Introductionmentioning

confidence: 99%

High-precision tracking and positioning for monitoring Holstein cattle

Luo,

Zhang,

Yuan

et al. 2024

PLoS ONE

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Enhanced animal welfare has emerged as a pivotal element in contemporary precision animal husbandry, with bovine monitoring constituting a significant facet of precision agriculture. The evolution of intelligent agriculture in recent years has significantly facilitated the integration of drone flight monitoring tools and innovative systems, leveraging deep learning to interpret bovine behavior. Smart drones, outfitted with monitoring systems, have evolved into viable solutions for wildlife protection and monitoring as well as animal husbandry. Nevertheless, challenges arise under actual and multifaceted ranch conditions, where scale alterations, unpredictable movements, and occlusions invariably influence the accurate tracking of unmanned aerial vehicles (UAVs). To address these challenges, this manuscript proposes a tracking algorithm based on deep learning, adhering to the Joint Detection Tracking (JDT) paradigm established by the CenterTrack algorithm. This algorithm is designed to satisfy the requirements of multi-objective tracking in intricate practical scenarios. In comparison with several preeminent tracking algorithms, the proposed Multi-Object Tracking (MOT) algorithm demonstrates superior performance in Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), and IDF1. Additionally, it exhibits enhanced efficiency in managing Identity Switches (ID), False Positives (FP), and False Negatives (FN). This algorithm proficiently mitigates the inherent challenges of MOT in complex, livestock-dense scenarios.

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A reliable unmanned aerial vehicle multi-target tracking system with global motion compensation for monitoring Procapra przewalskii

Zhang,

Zhao,

et al. 2024

Ecological Informatics

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Procapra Przewalskii Tracking Autonomous Unmanned Aerial Vehicle Based on Improved Long and Short-Term Memory Kalman Filters

Cited by 5 publications

References 28 publications

Highly Accurate and Reliable Tracker for UAV-Based Herd Monitoring

Highly Accurate and Reliable Tracker for UAV-Based Herd Monitoring

High-precision tracking and positioning for monitoring Holstein cattle

A reliable unmanned aerial vehicle multi-target tracking system with global motion compensation for monitoring Procapra przewalskii

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