Traffic event cognition and reasoning in videos is an important task that has
a 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 QA
based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs,
for benchmarking the cognitive capability of causal inference and event
understanding models in complex traffic scenarios. Specifically, we propose 6
challenging reasoning tasks corresponding to various traffic scenarios, so as
to evaluate the reasoning capability over different kinds of complex yet
practical traffic events. Moreover, we propose Eclipse, a novel Efficient
glimpse network via dynamic inference, in order to achieve
computation-efficient and reliable video reasoning. The experiments show that
our method achieves superior performance while reducing the computation cost
significantly. The project page: this https URL

本文提出了一种基于视频问答的交通事件认知数据集，用于基准测试因果推理和事件理解模型在复杂交通场景中的认知能力，并提出了一个针对各种交通场景的挑战性推理任务来评估不同类型的复杂但实用的交通事件的推理能力。此外，提出了一种新的高效视频推理模型，命名为 Eclipse，通过动态推理实现计算效率和可靠性的平衡。实验证明，我们的方法在显著降低计算成本的同时，实现了卓越的性能。