电子学报2025，Vol.53Issue(3)：728-743,16.DOI:10.12263/DZXB.20240727
时序信息引导跨视角特征融合的多无人机多目标跟踪方法
Temporal-Guided Cross-View Feature Fusion Network for Multi-Drone Multi-Object Tracking
摘要
Abstract
Multi-drone multi-object tracking aims to predict the tracklets and identities of all targets from videos si-multaneously captured by multiple drones,which alleviates the tracking performance degradation when individual drone videos suffer from challenges such as occlusion and cluttered backgrounds.However,the differences in viewpoints and scales of images captured by different drones are usually large,resulting in significant difficulties for aligning and fusing cross-drone features.To address this problem,we propose a novels tracker based on cross-view feature fusion guided by temporal information.It first designs an object-aware alignment network(OAN)that utilizes the tracklet prior during track-ing to estimate the transformation relationships between cross-drone frames at previous moments.Then,a temporal-aware alignment network(TAN)is constructed to explore the information of single-drone images in the before-and-after moments to fine-tune the transformation relationship across the images.Finally,based on the cross-drone image transformation rela-tionship estimated by OAN and TAN,this paper presents a cross-drone feature fusion network(CFFN)to fuse the visual in-formation captured by multiple drones,which mitigates the tracking performance degradation in complex scenes.Experi-mental results on the MDMT dataset show that the proposed TCFNet is more competitive than existing mainstream trackers,exceeding current state-of-the-art model by 2.23,1.67,and 2.15 percentage points in terms of tracking accuracy,identifica-tion F1 score,and multi-device association score.关键词
多无人机多目标跟踪/时序信息/轨迹先验/跨视角特征融合/准确跟踪Key words
multi-drone multi-object tracking/temporal information/tracklet prior/cross-view feature fusion/accu-rate tracking分类
信息技术与安全科学引用本文复制引用
伍瀚,孙浩,计科峰,匡纲要..时序信息引导跨视角特征融合的多无人机多目标跟踪方法[J].电子学报,2025,53(3):728-743,16.基金项目
国家自然科学基金(No.61971426) National Natural Science Foundation of China(No.61971426) （No.61971426）
