Domain Adaptive Driver Distraction Detection Based on Partial Feature Alignment and Confusion-Minimized Classification

The increased use of smartphones and in-vehicle infotainment systems leads to more distraction related accidents. Although numerous deep learning techniques have been developed to identify driver distraction based on images, they often perform poorly or even fail in cross-domain conditions. Retraining models on the target domain is a traditional solution, but it requires a significant number of manually annotated data, time, and computer resources. Therefore, this paper proposes a distance-based domain-adaptive approach for global feature matching. It lowers the
$\boldmath{\mathcal{H}}$
-divergence at the feature level for cross-domain classification. Specifically, a domain-adaptive algorithm is developed based on partial minimum classification confusion (PMCC) matching. The proposed method first predicts target image category weights using a classification network, and then regularizes them by minimizing the classification confusion. It subsequently employs the regularized category weights as pseudo-labels for target domain images, which are then aligned with identically labelled source domain image features. Three cross-domain distracted driving datasets are used to examine the proposed method, including State-farm, AUC-Real and AUC-Laboratory. The results show that our proposed strategy performs better than the state-of-the-art approaches, which provides a solution to further improve distraction detection performance in various situations.