无需真实事故数据，用合成视频训练实现交通事故的时空定位与类型识别
SynCrash: A Multi-Stage Pipeline for Zero-Shot Accident Detection and Localization in Traffic Surveillance Video
- 分三阶段：时序定位、空间定位、碰撞类型分类
- 在真实数据上达到72.3%定位准确率，91.5%碰撞类型识别率
- 适合交通监控系统开发者和自动驾驶安全研究者
我们提出SynCrash，一个用于固定视角交通监控视频中零样本事故检测、空间定位和碰撞类型分类的多阶段流水线。该方法针对CVPR 2026 ACCIDENT挑战，需在无真实标注训练数据的情况下预测事故发生时间、帧内撞击位置及碰撞类型。流程分为三个解耦阶段：(1) 基于CARLA生成的合成视频微调VideoMAEv2-giant骨干网络，结合元数据感知嵌入与密集滑窗推理，实现时序定位；(2) 使用YOLO进行目标检测，结合基于物理的混合启发式方法，利用边界框重叠与轨迹推理预测撞击点；(3) 通过轻量级规则策略，根据检测到车辆的数量与布局判断碰撞类型。关键洞察在于：时序理解依赖合成数据上的监督微调，而空间理解更受益于预训练检测器与物理先验在跨域间的自然迁移。
原文摘要 · Abstract (English)
We present SynCrash, a multi-stage pipeline for zero-shot accident detection, spatial localization, and collision-type classification in fixed-view CCTV surveillance video. Our approach addresses the ACCIDENT at CVPR 2026 Challenge, which requires predicting when an accident occurs, where in the frame the impact happens, and what type of collision it is, all without access to labeled real-world training data. The pipeline operates in three decoupled stages: (1) Temporal localization via a VideoMAEv2-giant backbone fine-tuned on CARLA-based synthetic clips with metadata-aware embeddings and dense sliding-window inference; (2) Spatial localization using YOLO for object detection combined with a physics-informed hybrid heuristic that leverages bounding-box overlap and trajectory-based reasoning to predict the impact point; and (3) Collision-type classification using a lightweight rule-based strategy derived from the number and configuration of detected vehicles. The key insight is that temporal understanding benefits from supervised fine-tuning on synthetic data, whereas spatial understanding is better served by pretrained object detectors and physics priors that transfer naturally across domains.
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。