Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis
This work addresses traffic safety analysis for accident prevention by improving video understanding, though it is incremental as it combines existing models with a task-specific optimization strategy.
The paper tackles traffic safety video analysis by developing a dual-model framework that separates training for captioning and visual question answering tasks, achieving a CIDEr score of 1.1001 for temporal reasoning and a VQA accuracy of 60.80% for visual understanding, with an S2 score of 45.7572 in the AI City Challenge.
Traffic safety analysis requires complex video understanding to capture fine-grained behavioral patterns and generate comprehensive descriptions for accident prevention. In this work, we present a unique dual-model framework that strategically utilizes the complementary strengths of VideoLLaMA and Qwen2.5-VL through task-specific optimization to address this issue. The core insight behind our approach is that separating training for captioning and visual question answering (VQA) tasks minimizes task interference and allows each model to specialize more effectively. Experimental results demonstrate that VideoLLaMA is particularly effective in temporal reasoning, achieving a CIDEr score of 1.1001, while Qwen2.5-VL excels in visual understanding with a VQA accuracy of 60.80\%. Through extensive experiments on the WTS dataset, our method achieves an S2 score of 45.7572 in the 2025 AI City Challenge Track 2, placing 10th on the challenge leaderboard. Ablation studies validate that our separate training strategy outperforms joint training by 8.6\% in VQA accuracy while maintaining captioning quality.