用双流网络分析行车记录仪中的险情视频，提升识别准确率。
Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos
- 采用模仿人脑快慢视觉通道的SlowFast网络
- 显著提高险情视频分类准确率，具体数值未提
- 适合交通安全研究与智能驾驶系统开发
本文利用模拟人类大脑M（巨细胞）和P（小细胞）通路处理视觉信息的慢速与快速流特性的SlowFast深度神经网络，对行车记录仪中的险情交通视频进行分类。该方法显著提升了交通险情视频分析的准确性，并为交通场景下的人类视觉感知提供了新见解。此外，研究有助于提升交通安全性，为交通事故中潜在的认知错误提供了新的分析视角。
原文摘要 · Abstract (English)
This paper classifies near-miss traffic videos using the SlowFast deep neural network that mimics the characteristics of the slow and fast visual information processed by two different streams from the M (Magnocellular) and P (Parvocellular) cells of the human brain. The approach significantly improves the accuracy of the traffic near-miss video analysis and presents insights into human visual perception in traffic scenarios. Moreover, it contributes to traffic safety enhancements and provides novel perspectives on the potential cognitive errors in traffic accidents.
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