An Online Multiobject Tracking Network for Autonomous Driving in Areas Facing Epidemic | IEEE Journals & Magazine | IEEE Xplore

An Online Multiobject Tracking Network for Autonomous Driving in Areas Facing Epidemic


Abstract:

Multi-object tracking is of great importance in autonomous driving. However, with the outbreak of COVID-19, multi-object tracking faces new challenges in areas gripped by...Show More

Abstract:

Multi-object tracking is of great importance in autonomous driving. However, with the outbreak of COVID-19, multi-object tracking faces new challenges in areas gripped by epidemics because of complex motion blur, frequent occlusions, and appearance deformations. To reliably improve object trajectory association in epidemic-plagued areas, we propose a temporal-spatial aggregation embedding network (TSAEN) for multi-object tracking. Our embedding network contains a temporal-aware correlation module (TACM) and spatial-aggregate embedding module (SAEM) that can fully obtain and aggregate appearance clues related to moving objects in previous frames. The TACM learns the temporal homogeneity features of the current and previous frames to perceive features with correlated appearance cues. Then, the SAEM adjusts the spatial deformation for each perceived temporal homogeneity feature and aggregates them for re-ID embedding learning. The experimental results demonstrate that our proposed method is able to achieve excellent overall performance.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 23, Issue: 12, December 2022)
Page(s): 25191 - 25200
Date of Publication: 16 August 2022

ISSN Information:

Funding Agency:


References

References is not available for this document.