SDTrack：基于脉冲神经网络的事件相机跟踪基线
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks
把全脉冲驱动的 SNN 骨干网络与 Transformer 跟踪头组合成端到端跟踪管线，并用 Global Trajectory Prompt 把全局轨迹信息聚合进事件帧，为在事件相机上做低功耗时序跟踪提供了可复用的架构与事件帧构造思路。
原文依据 the first Transformer-based Spike-Driven Tracking (SDTrack) pipeline
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论事件相机具备更优的时序分辨率、动态范围、能效与像素带宽。脉冲神经网络（SNN）通过离散脉冲信号与事件数据天然互补，使其成为事件相机跟踪的理想选择。然而，现有将人工神经网络（ANN）与 SNN 相结合的方法受限于次优的架构，既损害了能效，也限制了跟踪性能。为克服这些局限，我们提出首个基于 Transformer 的脉冲驱动跟踪（SDTrack）管线。该管线引入了一种新颖的事件帧聚合方法——全局轨迹提示（Global Trajectory Prompt, GTP），以及一个基于 Transformer 的跟踪器。GTP 方法有效捕获全局轨迹信息，并将其与事件流聚合为事件帧，以增强时空表征。基于 Transformer 的跟踪器由一个完全脉冲驱动的 SNN 骨干网络和一个简洁的跟踪头组成。SDTrack 管线端到端运行，无需数据增强或后处理。大量实验表明，我们的 SDTrack-Tiny 管线仅以 19.61M 参数和 8.16mJ 能耗即取得有竞争力的精度，而 Base 版本在三个数据集上取得了最优精度。我们的工作为未来神经形态视觉研究奠定了坚实基础。
英文原文摘要 1334 字符
Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based Spike-Driven Tracking (SDTrack) pipeline. It incorporates a novel event frame aggregation method called Global Trajectory Prompt (GTP) and a Transformer-based tracker. The GTP method effectively captures global trajectory information and aggregates it with event streams into event frames to enhance spatiotemporal representation. The Transformer-based tracker comprises a fully spike-driven SNN backbone and a simple tracking head. The SDTrack pipeline operates end-to-end without data augmentation or post-processing. Extensive experiments demonstrate that our SDTrack-Tiny pipeline achieves competitive accuracy with only 19.61M parameters and 8.16mJ energy consumption, while our Base version achieves state-of-the-art accuracy across three datasets. Our work establishes a solid foundation for future neuromorphic vision research.