文献类型：期刊文献

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 Research Institute; [2] Zhejiang University, China; [3] East China University of Science and Technology, China

年份：2022

卷号：2022-June

起止页码：3241

外文期刊名：IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

收录：EI(收录号：20223712741440)

语种：英文

外文关键词：Computer vision - Intelligent vehicle highway systems

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. ? 2022 IEEE.