无需标注，通过几何结构区分物体静止与独立运动
GeoIMO: Geometry-Driven Independent Motion Classification for Event Cameras
- 基于事件流直接建模自身运动，推断背景运动
- 局部运动偏离背景预测即判为移动，用尺度不变残差量化
- 不依赖学习与人工标签，适合自动驾驶场景
现有车载事件数据集依赖帧流水线的外观标注，难以支持运动感知。本文提出一种几何驱动、无标注的框架，通过事件流直接利用自身运动结构，将检测到的物体分类为静态或独立运动。采用带偏航补偿的扩张焦点模型估计全局背景运动，当局部运动偏离该预测时（以尺度不变残差量化），即判定为移动物体。时间稳定化提升连续事件窗口下的鲁棒性。该方法无需训练、无需人工运动标注，可适配任意输入边界框。在MVSEC和Prophesee 1 Megapixel Automotive Detection数据集上的实验表明，其在多样驾驶场景中表现稳定；偏航补偿在转弯时显著提升效果，而简单的平移局部模型则提供了良好的精度-效率平衡。
原文摘要 · Abstract (English)
Existing automotive event datasets rely on appearance-based annotations from frame pipelines, making them poorly suited for motion-aware event perception. We present a geometry-driven, annotation-free framework that classifies detected objects as static or independently moving by exploiting ego-motion structure directly from the event stream. A Focus of Expansion model with yaw compensation estimates global background motion, while objects are labeled as moving when local motion deviates from this prediction, as quantified by a scale-invariant residual. Temporal stabilization improves robustness across consecutive event windows. The method requires no learning, no manual motion labels, and works with any input bounding boxes. Experiments on MVSEC and the Prophesee 1 Megapixel Automotive Detection dataset demonstrate consistent performance across diverse driving scenarios, with yaw compensation improving results during turns and a simple translational local model offering a favorable accuracy-efficiency trade-off.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。