A pose-aware dynamic weighting model using feature integration for driver action recognition. (August 2022)

• Record Type: Journal Article
• Authors: Lu, Mingqi; Hu, Yaocong; Lu, Xiaobo
• Is Part Of: Engineering applications of artificial intelligence. Volume 113(2022)
• Publication Date: 2022-08
• Subjects: Driver action -- Dynamic convolution -- Feature fusion -- Channel attention -- Pose estimation; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104918
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Traffic accidents caused by distracted driving are on the rise, posing a serious threat to the safety of people's lives and property. Recognition and early warning of the driver's actions is particularly important. Considering the differences in local details of driver actions, we use the keypoint information of drivers that reflects the category differences. Specifically, we explicitly model keypoints features and propose a pose-aware driver action recognition model. We design a pose-based feature fusion module incorporating the attention mechanism, to fuse the global features and keypoint features of different scales in driving images. In addition, we propose an input-dependent weighting module to enhance the discrimination of the fused features. We use dynamic convolution to apply channel attention to convolution weights. According to the input driving image, the corresponding weights are adaptively generated for multiple convolution kernels, and the final weighted summation is carried out. This is a soft gate scheme, which opens a new perspective for driver action recognition. The proposed model achieves 90.7% and 95.3% accuracy on StateFarm dataset and SEU-Driving dataset, which are improved by 1.4% and 3.1% respectively compared to SOTA (MSA-CNN).