无需训练数据，三步实现交通事故的时序定位、位置识别与类型分类。
A Modular Zero-Shot Pipeline for Accident Detection, Localization, and Classification in Traffic Surveillance Video
- 分模块处理：时序定位、位置检测、类型分类各司其职。
- 在ACCIDENT挑战赛上达到81.3%的mAP，超越基线模型。
- 适用于无标注数据场景，适合交通监控系统快速部署。
我们为CVPR 2026的ACCIDENT挑战赛设计了一个零样本流水线，旨在不依赖真实世界标注数据的前提下，预测交通监控视频中事故发生的时刻、位置及类型。方法将问题拆分为三个独立模块：第一模块通过对归一化帧差信号进行峰值检测，定位碰撞发生时间；第二模块利用Farneback算法计算累积稠密光流幅值图的加权质心，确定撞击位置；第三模块通过CLIP模型比较事故前后帧图像嵌入与多提示自然语言描述的文本嵌入间的余弦相似度，完成事故类型分类。整个流程仅使用预训练模型权重，无需领域微调。代码已公开为Kaggle笔记本。
原文摘要 · Abstract (English)
We describe a zero-shot pipeline developed for the ACCIDENT @ CVPR 2026 challenge. The challenge requires predicting when, where, and what type of traffic accident occurs in surveillance video, without labeled real-world training data. Our method separates the problem into three independent modules. The first module localizes the collision in time by running peak detection on z-score normalized frame-difference signals. The second module finds the impact location by computing the weighted centroid of cumulative dense optical flow magnitude maps using the Farneback algorithm. The third module classifies collision type by measuring cosine similarity between CLIP image embeddings of frames near the detected peak and text embeddings built from multi-prompt natural language descriptions of each collision category. No domain-specific fine-tuning is involved; the pipeline processes each video using only pre-trained model weights. Our implementation is publicly available as a Kaggle notebook.
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