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

Zhang, Guoqing; Zhao, Yongxiang; Fu, Ping; Luo, Wei; Shao, Quanqin; Zhang, Tongzuo; Yu, Zhongde

doi:10.1016/j.ecoinf.2024.102556

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Other1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Ungulates conservation in the face of human development: Mining and roads' influences on habitat and connectivity in Iran's central plateau

Mohammadi,

Almasieh,

Vaissi

2024

Ecological Informatics

View full text Add to dashboard Cite

Ungulates conservation in the face of human development: Mining and roads' influences on habitat and connectivity in Iran's central plateau

Mohammadi,

Almasieh,

Vaissi

2024

Ecological Informatics

View full text Add to dashboard Cite

FocusTrack: multiple object tracking of unmanned aerial vehicle by associating interested semantic information

Lu,

Wang,

et al. 2024

Sixteenth International Conference on Digital Image Processing (ICDIP 2024)

View full text Add to dashboard Cite

Multiple Object Tracking (MOT) is a classical task in the field of computer vision, which aims to identify and track all objects in a video scene and assign a unique ID number to each object. Tracking-by-Detection (TBD) paradigm has become the mainstream framework for MOT due to its high Tracking accuracy. With the development of UAV technology, MOT research for UAV video has important military and civilian value. However, it faces challenges such as class imbalance, many small targets, and occlusion of targets in the scene, which makes it difficult to correctly match and continuously track the targets. We propose a new algorithm for the MOT problem in the UAV scenario. On the one hand, to solve the class imbalance problem of small targets, a dynamic adjustment parameter adjustment method based on the gradient information of training samples is proposed to improve the generalization ability of traditional loss function in multi-class target tracking. On the other hand, to improve the accuracy of inter-frame matching, this paper introduces a new feature similarity calculation method, which is based on the Wasserstein distance and optimizes the matching process according to the weight allocation mechanism of feature importance. Finally, the effectiveness of the proposed algorithm is verified on the VisDroneMOT2019 dataset. The results show that compared with the existing MOT algorithm, the proposed algorithm has significant improvements in tracking accuracy, trajectory integrity and identity maintenance, achieving 38.8% MOTA and 52.8% IDF1, which are better than the existing state-of-the-art tracking algorithms.

show abstract

Lightweight Sheep Counting Study Based on Mask R-CNN

杨

2024

AIRR

View full text Add to dashboard Cite

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

Cited by 3 publications

References 26 publications

Ungulates conservation in the face of human development: Mining and roads' influences on habitat and connectivity in Iran's central plateau

Ungulates conservation in the face of human development: Mining and roads' influences on habitat and connectivity in Iran's central plateau

FocusTrack: multiple object tracking of unmanned aerial vehicle by associating interested semantic information

Lightweight Sheep Counting Study Based on Mask R-CNN

Contact Info

Product

Resources

About