Enhancing Traffic Safety with Parallel Dense Video Captioning for
  End-to-End Event Analysis
Maged Shoman Dongdong Wang Armstrong Aboah Mohamed Abdel-Aty
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 https://github.com/UCF-SST-Lab/AICity2024CVPRW