农业机械学报2025，Vol.56Issue(5)：475-481,491,8.DOI:10.6041/j.issn.1000-1298.2025.05.045
融合深度信息与运动趋势的羊只多目标跟踪方法
Sheep Multi-object Tracking Method Integrating Depth Information and Motion Trends
摘要
Abstract
In recent years,the application of information technology in sheep farming has become increasingly sophisticated,necessitating more accurate individual identification and behavior monitoring.This,in turn,has placed higher demands on the accuracy of multiple object tracking(MOT)algorithms,which formed the foundation of these applications.However,existing MOT algorithms often underperformed in scenarios involving object occlusion and dynamic environments.Two novel tracking cues,depth modulated IoU(DIoU)and tracklet direction modeling(TDM),was proposed,aiming at enhancing the precision and robustness of multiple object tracking by supplementing the intersection over union(IoU)cue.DIoU improved the traditional IoU calculation by incorporating depth information of the objects.TDM focused on the movement trends of targets,predicting their future directions based on their historical movement patterns.The DIoU and TDM strategies were integrated into the BoT-SORT algorithm,resulting in an improved multiple object tracking algorithm.Evaluations on two datasets showed that the enhanced algorithm increased the multiple object tracking accuracy(MOTA)by 1.6 percentage points and 1.7 percentage points and the identification F1 score(IDF1)by 1.9 percentage points and 1.0 percentage points,respectively,compared with baseline methods.These results indicated that the improved algorithm significantly enhanced tracking continuity and accuracy in complex scenarios.This research provided insights and methods for multiple object tracking technology,holding significant implications for practical applications.关键词
多目标跟踪/识别/羊只/BoT-SORT/数据关联/目标检测Key words
multi-object track/recognition/sheep/BoT-SORT/data association/object detection分类
信息技术与安全科学引用本文复制引用
王美丽,杨恩德..融合深度信息与运动趋势的羊只多目标跟踪方法[J].农业机械学报,2025,56(5):475-481,491,8.基金项目
陕西省秦创原"科学家+工程师"建设项目(2022KXJ-67) （2022KXJ-67）
