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pro vyhledávání: '"Ding, Shuxiao"'
Many query-based approaches for 3D Multi-Object Tracking (MOT) adopt the tracking-by-attention paradigm, utilizing track queries for identity-consistent detection and object queries for identity-agnostic track spawning. Tracking-by-attention, however
Externí odkaz:
http://arxiv.org/abs/2405.08909
Tracking 3D objects accurately and consistently is crucial for autonomous vehicles, enabling more reliable downstream tasks such as trajectory prediction and motion planning. Based on the substantial progress in object detection in recent years, the
Externí odkaz:
http://arxiv.org/abs/2308.06635
Autor:
Li, Peizheng, Ding, Shuxiao, Chen, Xieyuanli, Hanselmann, Niklas, Cordts, Marius, Gall, Juergen
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic. While bird's-eye view (BEV) representations are commonplace in perception for autonom
Externí odkaz:
http://arxiv.org/abs/2306.10761
We present our approach to unsupervised domain adaptation for single-stage object detectors on top-view grid maps in automated driving scenarios. Our goal is to train a robust object detector on grid maps generated from custom sensor data and setups.
Externí odkaz:
http://arxiv.org/abs/2002.00667