Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos

Yucheng Zhang; Koichi Emura; Eiji Watanabe

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arXiv · 2412.03903

Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos

Yucheng Zhang·Koichi Emura·Eiji Watanabe

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Abstract

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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Yucheng Zhang, Koichi Emura, Eiji Watanabe. 2024-12-05. Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos. https://arxiv.org/abs/2412.03903

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