文献类型：会议论文

英文题名：MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving

作者：Li, Wei[1];Chen, Shimin[1];Gu, Jianyang[1,2];Wang, Ning[1,3];Chen, Chen[1];Guo, Yandong[1]

机构：[1]OPPO Res Inst, Beijing, Peoples R China;[2]Zhejiang Univ, Hangzhou, Peoples R China;[3]East China Univ Sci & Technol, Shanghai, Peoples R China

会议论文集：IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

会议日期：JUN 18-24, 2022

会议地点：New Orleans, LA

语种：英文

摘要：Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in naturalistic driving. Specifically, we adopted SwinTransformer as feature extractor, and a single framework to detect boundary and class at the same time. Also, we improve multiple loss function for explicit constraints of embedded feature distributions. Our proposed framework achieves the overall F1 -score of 0.3154 on A2 dataset.