A Coarse-To-Fine Boundary Localization Method for Naturalistic Driving Action Recognition

Guanchen Ding, Wenwei Han, Chenglong Wang, Mingpeng Cui, Lin Zhou, Dianbo Pan, Jiayi Wang, Junxi Zhang, Zhenzhong Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 3234-3241

Naturalistic driving action recognition plays an important role in understanding drivers' distraction behavior in the traffic environment. The main challenge of this task is the accurate localization of the temporal boundary for each distraction driving behavior in the video. Although many temporal action localization methods can identify action classes, it is difficult to predict accurate temporal boundaries for this task since the driving actions of the same category usually present large intra-class variation. In this paper, we introduce a Coarse-to-Fine Boundary Localization method called CFBL, which obtains fine-grained temporal boundaries progressively through three stages. Concretely, in the first coarse boundary generation stage, we adopt a modified anchor-free model Anchor-Free Saliency-based Detector (AFSD) to make an interval estimation of the temporal boundaries of distraction behavior. In the second boundary refinement stage, we use the Dense Boundary Generation (DBG) model to adjust the estimated interval of the temporal boundaries. In the final boundary decision stage, we build a Localization Boundary Refinement Module to determine the final boundaries of different actions. Besides, we adopt a voting strategy to combine the results of different camera views to enhance the model's distraction driving action classification ability. The experiments conducted on the Track 3 validation set of the 2022 AI City Challenge demonstrate competitive performance of the proposed method.