An Online Multiobject Tracking Network for Autonomous Driving in Areas Facing Epidemic
IEEE Transactions on Intelligent Transportation Systems
; : 1-10, 2022.
Article
in English
| Scopus | ID: covidwho-2019013
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. IEEE
Aggregates; autonomous driving; Correlation; Detectors; epidemic areas; Epidemics; Feature extraction; Multi-object tracking; re-ID embedding; Strain; Target tracking; Deformation; Epidemiology; Object detection; Object recognition; Embedding network; Embeddings; Epidemic; Epidemic area; Features extraction; Targets tracking
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Transactions on Intelligent Transportation Systems
Year:
2022
Document Type:
Article
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