https://arxiv.org/abs/2212.05598 Skip to main content Cornell University We are hiring We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate arxiv logo > cs > arXiv:2212.05598 [ ] Help | Advanced Search [All fields ] Search arXiv logo Cornell University Logo [ ] GO quick links * Login * Help Pages * About Computer Science > Computer Vision and Pattern Recognition arXiv:2212.05598 (cs) [Submitted on 11 Dec 2022 (v1), last revised 25 May 2023 (this version, v3)] Title:Recurrent Vision Transformers for Object Detection with Event Cameras Authors:Mathias Gehrig, Davide Scaramuzza Download a PDF of the paper titled Recurrent Vision Transformers for Object Detection with Event Cameras, by Mathias Gehrig and Davide Scaramuzza Download PDF Abstract: We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: First, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (<12 ms on a T4 GPU) and favorable parameter efficiency (5 times fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2212.05598 [cs.CV] (or arXiv:2212.05598v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2212.05598 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, 2023 Submission history From: Mathias Gehrig [view email] [v1] Sun, 11 Dec 2022 20:28:59 UTC (3,389 KB) [v2] Mon, 3 Apr 2023 16:38:14 UTC (3,392 KB) [v3] Thu, 25 May 2023 09:17:11 UTC (3,392 KB) Full-text links: Download: * Download a PDF of the paper titled Recurrent Vision Transformers for Object Detection with Event Cameras, by Mathias Gehrig and Davide Scaramuzza PDF * Other formats [by-4] Current browse context: cs.CV < prev | next > new | recent | 2212 Change to browse by: cs References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... 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