TY - JOUR
T1 - Learning a Robust Topological Relationship for Online Multiobject Tracking in UAV Scenarios
AU - Deng, Chenwei
AU - Wu, Jiapeng
AU - Han, Yuqi
AU - Wang, Wenzheng
AU - Chanussot, Jocelyn
N1 - Publisher Copyright:
© 1980-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - Many existing multiobject tracking (MOT) methods tend to model each object's feature individually. However, under acute viewpoint variation and occlusion, there may exist significant differences between the current and historical features of objects, which easily leads to object loss. To alleviate these issues, the topological relationships (i.e., geometric shapes formed by objects) should be modeled as a supplement to individual object features to maintain stability. In this article, we propose a novel MOT framework, which consists of a frame graph and association graph, to leverage the topological relationships both spatially and temporally. Technically, the frame graph models distance and angle among objects to resist viewpoint change, while the association graph utilizes the interframe temporal consistency of topological features to recover occluded objects. Extensive experiments on mainstream datasets demonstrate the effectiveness.
AB - Many existing multiobject tracking (MOT) methods tend to model each object's feature individually. However, under acute viewpoint variation and occlusion, there may exist significant differences between the current and historical features of objects, which easily leads to object loss. To alleviate these issues, the topological relationships (i.e., geometric shapes formed by objects) should be modeled as a supplement to individual object features to maintain stability. In this article, we propose a novel MOT framework, which consists of a frame graph and association graph, to leverage the topological relationships both spatially and temporally. Technically, the frame graph models distance and angle among objects to resist viewpoint change, while the association graph utilizes the interframe temporal consistency of topological features to recover occluded objects. Extensive experiments on mainstream datasets demonstrate the effectiveness.
KW - Feature association
KW - graph neural networks
KW - multiobject tracking (MOT)
KW - topological features
KW - unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85196516163&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2024.3416326
DO - 10.1109/TGRS.2024.3416326
M3 - Article
AN - SCOPUS:85196516163
SN - 0196-2892
VL - 62
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
M1 - 5628615
ER -