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

• URL: http://arxiv.org/abs/2103.15538v2
• Date: Tue, 30 Mar 2021 15:00:27 GMT
• Title: TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events
• Authors: Li Xu, He Huang and Jun Liu
• Abstract summary: We create a novel dataset, TrafficQA (Traffic Question Answering), based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs. 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. We also propose Eclipse, a novel Efficient glimpse network via dynamic inference, in order to achieve computation-efficient and reliable video reasoning.
• Score: 13.46045177335564
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, 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: https://github.com/SUTDCV/SUTD-TrafficQA.

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