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Traffic event cognition and reasoning in videos is an important task that hasa wide range of applications in intelligent transportation, assisted driving,and autonomous vehicles. In this paper, we create a novel dataset,SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QAbased on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs,for benchmarking the cognitive capability of causal inference and eventunderstanding models in complex traffic scenarios. Specifically, we propose 6challenging reasoning tasks corresponding to various traffic scenarios, so asto evaluate the reasoning capability over different kinds of complex yetpractical traffic events. Moreover, we propose Eclipse, a novel Efficientglimpse network via dynamic inference, in order to achievecomputation-efficient and reliable video reasoning. The experiments show thatour method achieves superior performance while reducing the computation costsignificantly. The project page: https://github.com/SUTDCV/SUTD-TrafficQA.

SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events