自适应时空窗口：释放事件相机在异构速度场景中的潜力
Adaptive Spatial-Temporal Window: Unlocking the Potential of Event Cameras in Heterogeneous Velocity Scenarios
提出基于最大熵原理的事件流自适应时空窗口划分策略，可替代固定时间/固定计数的事件分组方式，直接用于事件相机下游任务（如目标检测、跟踪）的预处理环节，提升异构速度场景下的特征质量。
原文依据 we propose Adaptive Spatial-Temporal Window (ASTW) strategy, which simultaneously achieves temporal adaptivity and spatial locality in event partitioning.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论大多数基于事件的算法通常将事件流分割成固定组（例如固定时间或固定计数）以供下游处理，缺乏对场景动态的自适应性。已有若干自适应划分策略被提出，但它们无法很好地应对同时包含快速和慢速运动目标的异构速度场景（HVS）。为解决这一问题，我们提出了自适应时空窗口（ASTW）策略，在事件划分中同时实现时间自适应性和空间局部性。基于最大熵原理，我们推导了块级时间窗口确定准则，并基于事件密度和向量化计算高效实现。在公开的基于事件的目标检测和跟踪数据集上的实验表明，ASTW显著优于现有的最优划分策略。我们还构建了HetVel，这是首个面向HVS的RGB-事件双模态数据集，并进一步突出了ASTW在这一具有挑战性的基准上的优势。我们相信，我们的ASTW策略和构建的HetVel数据集将推动神经形态视觉领域的发展。
英文原文摘要 1176 字符
Most event-based algorithms typically split the event stream into fixed groups (e.g., fixed time or fixed count) for downstream processing, lacking adaptivity to scene dynamics. Several adaptive partitioning strategies have been proposed, but they are unable to cope well with heterogeneous velocity scenarios (HVS) involving both fast- and slow-moving objects. To address this issue, we propose Adaptive Spatial-Temporal Window (ASTW) strategy, which simultaneously achieves temporal adaptivity and spatial locality in event partitioning. Based on the principle of maximum entropy, we derive a patch-level time window determination criterion and efficiently implement it based on event density and vectorized calculations. Experiments on publicly available event-based object detection and tracking datasets demonstrate that ASTW significantly outperforms existing state-of-the-art partitioning strategies. We also construct HetVel, the first RGB-event dual-modality dataset for HVS, and further highlight the advantages of ASTW on this challenging benchmark. We believe that our ASTW strategy and the constructed HetVel dataset will advance the field of neuromorphic vision.