This paper introduces our solution for Track 2 in AI City Challenge 2024. The
task aims to solve traffic safety description and analysis with the dataset of
Woven Traffic Safety (WTS), a real-world Pedestrian-Centric Traffic Video
Dataset for Fine-grained Spatial-Temporal Understanding. Our solution mainly
focuses on the following points: 1) To solve dense video captioning, we
leverage the framework of dense video captioning with parallel decoding (PDVC)
to model visual-language sequences and generate dense caption by chapters for
video. 2) Our work leverages CLIP to extract visual features to more
efficiently perform cross-modality training between visual and textual
representations. 3) We conduct domain-specific model adaptation to mitigate
domain shift problem that poses recognition challenge in video understanding.
4) Moreover, we leverage BDD-5K captioned videos to conduct knowledge transfer
for better understanding WTS videos and more accurate captioning. Our solution
has yielded on the test set, achieving 6th place in the competition. The open
source code will be available at this https URL

该研究介绍了在 AI City Challenge 2024 中针对 Track 2 的解决方案，主要关注交通安全、视频字幕、跨模态训练和领域特定模型适应等关键词。该解决方案在竞赛中取得了第六名，并通过提供开源代码使其更广泛地应用。