A Multi-Convolutional Stream for Hybrid network for Driver Action Recognition at Nighttime
Karam Abdullah, Imen Jegham, Anouar Ben Khalifa, Mohamed Ali Mahjoub · 2022 8th International Conference on Control, Decision and Information Technologies (CoDIT) · 2022
Driver monitoring at nighttime is a tremendous area of research because of its crucial role to save lives and decrease traffic crashes injuries. However, this task is highly complex because of the high amount of naturalistic driving issues and the low visibility. In the gist of this paper, a novel nighttime driver action recognition network named multi-convolutional stream for hybrid network are proposed, which effectively fuses multimodal data to efficiently classify driver's actions in low visibility and a cluttered driving scene. Using the unique public driver action dataset recorded at nighttime, up to our knowledge, in two separate viewpoints, our proposed methodology beats state-of-the-art methodologies in classification performance, with an advancement of up to 10% over the best-practice methods.