计算机工程与应用2026，Vol.62Issue(11)：272-283,12.DOI:10.3778/j.issn.1002-8331.2503-0216
考虑跨空间特征重构的行人过街动作检测方法
Pedestrian Crossing Behavior Detection Method Considering Cross-Spatial Feature Reconstruction
摘要
Abstract
Pedestrians,as vulnerable road users,face elevated risks in traffic environments where hazardous crossing behav-iors constitute a primary contributor to road accidents.Aiming at the problem of action feature extraction,inefficient feature fusion and feature information loss caused by multi-scale and occlusion of pedestrians under the monitoring perspective,a pedestrian's crossing behavior detection network(PCBDNet)based on cross-spatial feature reconstruction is proposed.The backbone network uses a cross-space parallel subnetwork feature structure to enhance the network's ability to learn multi-scale pedestrian features.The neck network fuses features using spatial and channel reconstruction convolutional modules to reduce redundant computation and facilitate the learning of pedestrian action features,and constructs feature enhancers to separate features and perform cross-spatial reconstruction to reduce information loss due to occlusion.The head network uses repulsion loss to improve the coordinate loss function to further improve occlusion target detection accuracy.Experiments show that the mAP@0.5 reaches 89.6%,the mAP@[0.5:0.95]reaches 72.4%,the model size is moderate,and the FPS remains high.The feature visualization experiments show that PCBDNet pays more attention to pedestrian crossing actions,which provides a new solution to enhance the risk reduction of traffic accidents.关键词
智能交通/行人动作检测/跨空间特征重构/注意力机制/交通安全Key words
intelligent transport/pedestrian behavior detection/cross-space feature reconstruction/attention mechanism/traffic safety分类
信息技术与安全科学引用本文复制引用
陈思宇,何永福,谢世维,张浩池..考虑跨空间特征重构的行人过街动作检测方法[J].计算机工程与应用,2026,62(11):272-283,12.基金项目
国家自然科学基金青年科学基金(52202490) （52202490）
重庆交通大学研究生科研创新项目(CYS240479). （CYS240479）
