A video-text retrieval model for intelligent transportation systems based on cross-attention mechanism

This study introduces a novel cross-modality retrieval model leveraging a cross-attention mechanism to enhance the accuracy and efficiency of retrieving information by seamlessly integrating traffic surveillance video data with textual descriptions, such as traffic violation records or incident narratives. A comprehensive review of deep learning and attention mechanisms within intelligent transportation systems (ITS) highlights the critical role of multimodal models and large-scale datasets in tackling ITS-related challenges. The proposed model architecture is composed of several core components, including data preprocessing and feature extraction, cross-attention mechanisms, feature fusion and updating, fine-grained similarity measurement, as well as loss function design and optimization strategies. Experimental evaluations demonstrate that the proposed model achieves superior performance compared to existing approaches on transportation-specific datasets. Furthermore, ablation studies validate the contribution of each module to the model’s overall performance. Future work will focus on developing more efficient fusion methods and optimization algorithms to further improve the model’s capability and suitability for real-time traffic management applications.