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This is a Quantitative Study study.

October 18, 2025Open Access

Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis

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The dual-model framework improves traffic safety video analysis by separating tasks for better specialization.
VideoLLaMA achieves a CIDEr score of 1.1001, excelling in temporal reasoning for video captioning tasks.
Qwen2.5-VL outperforms in visual understanding with a VQA accuracy of 60.80%, showcasing the models' complementary strengths.
The method ranks 10th in the 2025 AI City Challenge, validating its effectiveness with an S2 score of 45.7572 on the WTS dataset.

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This analysis showcases a dual-model framework leveraging VideoLLaMA and Qwen2.5-VL for enhanced traffic safety, suggesting optimization strategies.

Kyem et al. (2025) studied this question.

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This analysis showcases a dual-model framework leveraging VideoLLaMA and Qwen2.5-VL for enhanced traffic safety, suggesting optimization strategies.