Siamese Network for Multi-View Driver Monitoring in Realistic Driving Settings

Human Action Recognition (HAR) is integral to various domains, particularly in intelligent transportation systems where real-time understanding of driver behavior is critical for road safety. Accurately recognizing driver actions is challenging due to inter-class similarity, viewpoint variation, and real-world occlusions. This paper proposes a multi-view HAR approach utilizing a Siamese neural network architecture, fine-tuned with DenseNet201, to learn discriminative feature embeddings across front and side perspectives. Evaluations on the challenging 3MDAD dataset demonstrate the robustness of our method against viewpoint and modality shifts, achieving 58.22