SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events (2103.15538v3)

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, 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: https://github.com/SUTDCV/SUTD-TrafficQA.

• The paper introduces SUTD-TrafficQA, a dataset of 10,080 videos and 62,535 QA pairs for traffic reasoning, and Eclipse, an efficient video question answering network.
• The SUTD-TrafficQA dataset includes six challenging reasoning tasks such as event forecasting, counterfactual inference, and attribution, designed to evaluate deeper causal understanding.
• The Eclipse network uses dynamic frame selection and adaptive inference to achieve state-of-the-art accuracy with significantly reduced computational costs for video reasoning.

An Analysis of SUTD-TrafficQA Dataset and Eclipse Network for Traffic Event Video Reasoning

The paper "SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events" presents significant advancements in video reasoning regarding traffic events. This work is anchored in the creation of a novel dataset, SUTD-TrafficQA, alongside the development of an efficient system, named Eclipse, tailored for video-based question answering (QA) in complex traffic scenarios.

Benchmark Dataset: SUTD-TrafficQA

SUTD-TrafficQA is introduced as a comprehensive dataset designed for benchmarking cognitive capabilities related to causal inference and understanding of traffic events. It comprises 10,080 annotated in-the-wild videos and 62,535 QA pairs, providing a robust basis for evaluating models tasked with spatio-temporal and logical reasoning.

The dataset is structured around six challenging reasoning tasks, which include:

• Basic Understanding: For assessing comprehension of fundamental visual features and events.
• Event Forecasting: Focusing on predicting future traffic events.
• Reverse Reasoning: For deducing prior events based on current video segments.
• Counterfactual Inference: Testing hypothetical scenarios within traffic situations.
• Introspection: Targeting reflective tasks that provide advice to prevent incidents.
• Attribution: Identifying the causal events leading to traffic incidents.

This categorization fosters diverse reasoning challenges, pushing models beyond surface-level understanding into deeper causal reasoning and inference.

Eclipse Network: Computational Efficiency and Dynamic Inference

Eclipse, the proposed network, represents an innovative leap in computational efficiency and adaptive reasoning techniques. Key features of Eclipse include dynamic frame selection and adaptive feature computation to address the challenges of analyzing lengthy video sequences typical in traffic videos.

Core Modules in Eclipse Network

1. QA Bank: An encoding mechanism providing comprehensive textual guidance embedding for dynamic reasoning.
2. Interaction Module: Integrates video frame features with QA embeddings to form a rich representation for further reasoning.
3. Glimpse-Determination Module: A novel approach utilizing joint Gumbel-Softmax operation to optimize frame selection and computational granularity, ensuring efficient resource use.
4. Exit-Policy Module: Offers adaptive inference, enabling the network to determine the optimal point to finalize reasoning, akin to human perception strategies.

Through this multifaceted approach, Eclipse attains superior reasoning accuracy while significantly reducing computation costs.

Experimental Validation

Empirical results underscore Eclipse’s efficacy, showcasing its ability to outperform existing models like TVQA and HCRN in both accuracy and computational efficiency. The work validates Eclipse against human performance benchmarks, highlighting its potential and current limitations. Notably, Eclipse achieves state-of-the-art performance with substantial reductions in GFLOPs per video, demonstrating its computationally efficient design.

Implications and Future Perspectives

The research has profound implications for fields such as autonomous driving, traffic management, and intelligent transportation systems, laying groundwork for more reliable and efficient video reasoning models. By addressing both the prediction accuracy and computational demands, algorithms derived from Eclipse can facilitate real-time decision-making in dynamic and complex environments like urban traffic systems.

Future research can expand upon Eclipse’s framework by integrating more sophisticated models of logical reasoning and further optimizing its inference mechanisms. Additionally, research can explore extending the dataset's range to include more diverse traffic conditions and scenarios, augmenting the depth of reasoning tasks.

Conclusion

This paper significantly advances the domain of video reasoning in traffic contexts by introducing the SUTD-TrafficQA dataset and Eclipse network. Though not labeled as revolutionary, the contributions are impactful for developing computationally efficient, adaptive reasoning systems tailored to complex spatio-temporal tasks. Researchers are encouraged to leverage this dataset and model framework to propel further innovation within intelligent transportation and autonomous systems.