PDF(Pubmed)
Abstract：
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.
摘要：
增强动物福利已成为当代精准畜牧业的关键要素，牛监测构成了精准农业的一个重要方面。近年来智能农业的发展大大促进了无人机飞行监控工具和创新系统的集成，利用深度学习来解释牛的行为。智能无人机,配备监控系统，已经发展成为野生动物保护和监测以及畜牧业的可行解决方案。然而，挑战出现在实际和多方面的牧场条件下，在尺度改变的地方，不可预测的运动，和遮挡总是影响无人机（UAV）的准确跟踪。为了应对这些挑战，这篇手稿提出了一种基于深度学习的跟踪算法，坚持CenterTrack算法建立的联合检测跟踪(JDT)范式。该算法旨在满足复杂实际场景下多目标跟踪的要求。与几种杰出的跟踪算法相比，提出的多目标跟踪(MOT)算法在多目标跟踪精度(MOTA)方面表现出卓越的性能，多目标跟踪精度(MOTP),IDF1此外,它在管理身份交换机(ID)方面表现出更高的效率，假阳性（FP），错误否定（FN）。该算法熟练地缓解了MOT在复杂环境中的固有挑战，牲畜密集的场景。
