Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring
systems. In real-world applications, it is common for vehicle cabins to be
equipped with cameras featuring different modalities. However, multi-modality
fusion strategies for the DAR task within car cabins have rarely been studied.
In this paper, we propose a novel yet efficient multi-modality driver action
recognition method based on dual feature shift, named DFS. DFS first integrates
complementary features across modalities by performing modality feature
interaction. Meanwhile, DFS achieves the neighbour feature propagation within
single modalities, by feature shifting among temporal frames. To learn common
patterns and improve model efficiency, DFS shares feature extracting stages
among multiple modalities. Extensive experiments have been carried out to
verify the effectiveness of the proposed DFS model on the Drive\&Act dataset.
The results demonstrate that DFS achieves good performance and improves the
efficiency of multi-modality driver action recognition.

基于双特征位移的新型高效多模态驾驶员动作识别方法 DFS，通过模态特征相互作用和时间帧之间的特征位移实现跨模态综合特征，共享多个模态中的特征提取阶段，以学习常见模式并提高模型效率，在 Drive&Act 数据集上验证了 DFS 模型的有效性和优良性能。