Evidence map›Paper›PMID 38714785›Full record

ArticleScientific reports2024

An efficient visual servo tracker for herd monitoring by UAV.

Wei Luo, Guoqing Zhang, Quanqin Shao, Yongxiang Zhao, Dongliang Wang, Xiongyi Zhang, Ke Liu, Xiaoliang Li, Jiandong Liu, Penggang Wang and 4 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Wei LuoNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Guoqing ZhangNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Quanqin ShaoKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Yongxiang ZhaoNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Dongliang WangKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. wangdongliang@igsnrr.ac.cn.
Xiongyi ZhangKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Ke LiuNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Xiaoliang LiNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Jiandong LiuNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Penggang WangNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Lin LiNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Guanwu WangNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Fulong WangNorth China Institute of Aerospace Engineering, Langfang, 065000, China.
Zhongde YuNorth China Institute of Aerospace Engineering, Langfang, 065000, China.

Funding

Hebei Provincial Department of Science and Technology 236Z7201GNational Natural Science Foundation of China 42071289
6 · The paper itself

Abstract

It is a challenging and meaningful task to carry out UAV-based livestock monitoring in high-altitude (more than 4500 m on average) and cold regions (annual average - 4 °C) on the Qinghai Tibet Plateau. The purpose of artificial intelligence (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 Deep SORT algorithms for target detection and tracking, respectively. In response to the problems encountered in the tracking process of complex and dense scenes, our work (1) Uses optical flow to compensate the Kalman filter, to solve the problem of mismatch between the target bounding box predicted by the Kalman filter (KF) and the input when the target detection in the current frame is complex, thereby improving the prediction accuracy; (2) Using a low confidence trajectory filtering method to reduce false positive trajectories generated by Deep SORT, thereby mitigating the impact of unreliable detection on target tracking. (3) A visual servo controller has been designed for the Unmanned Aerial Vehicle (UAV) to reduce the impact of rapid movement on tracking and ensure that the target is always within the field of view of the UAV camera, thereby achieving automatic tracking tasks. Finally, the system was tested using Tibetan yaks on the Qinghai Tibet Plateau as tracking targets, and the results showed that the system has real-time multi tracking ability and ideal visual servo effect in complex and dense scenes.

Identifiers

PMID38714785
PMCPMC11582714

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.