Context-Aware Streaming Perception in Dynamic Environments

نویسندگان

چکیده

AbstractEfficient vision works maximize accuracy under a latency budget. These evaluate offline, one image at time. However, real-time applications like autonomous driving operate in streaming settings, where ground truth changes between inference start and finish. This results significant drop. Therefore, recent work proposed to settings on average. In this paper, we propose for every environment context. We posit that scenario difficulty influences the initial (offline) difference, while obstacle displacement scene affects subsequent degradation. Our method, Octopus, uses these properties select configurations test method improves tracking performance (S-MOTA) by \(7.4\%\) over conventional static approach. Further, improvement using our comes addition to, not instead of, advances offline accuracy.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2022

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-031-19839-7_36