交通信息与安全2024，Vol.42Issue(6)：95-102,8.DOI:10.3963/j.jssn.1674-4861.2024.06.010
基于改进时间金字塔网络的出租车乘客上下车动作识别模型
A Recognition Model for Passenger Boarding and Alighting Action Based on Improved Temporal Pyramid Network
摘要
Abstract
Traditional algorithms for identifying illegal passenger-carrying behavior,which rely on image process-ing techniques,utilize manually crafted human-vehicle interaction rules to discern boarding and alighting actions.However,these rule sets often fall short due to the intricate nature of traffic scenarios,resulting in suboptimal recog-nition performance.Therefore,a deep learning model based on a temporal pyramid network(TPN)is introduced for boarding and alighting action recognition.By training on a large dataset,more complete features of taxi passenger boarding and alighting behaviors are extracted to improve recognition accuracy.To address the issue of the TPN model not distinguishing between driver and passenger roles,the output layer is redesigned based on door area per-ception.This modification enhances the efficiency of multi-dimensional feature extraction.To tackle the issue of the large spatiotemporal span in boarding and alighting actions,which leads to interference from irrelevant move-ments,a sliding window mechanism is introduced.This mechanism,based on dynamic window weights,captures key video frames of the actions,enhancing recognition efficiency.Based on the above improvement measures,a boarding and alighting neural network(BANN)model,based on door area perception and dynamic weights,is pro-posed to efficiently and accurately recognize illegal passenger-carrying behaviors.A training dataset with 4,047 an-notated video clips and a test dataset with 810 unannotated video clips are constructed for model performance vali-dation based on surveillance videos from Beijing Capital Airport.Experimental results demonstrate that the BANN model achieves precision and recall rates of 90.21%and 88.53%,respectively,representing improvements of 9.78%and 11.04%over the baseline TPN model.These results indicate that the BANN model can effectively meet the needs of traffic order supervision in transportation hubs.关键词
智能交通/上下车动作识别/乘客上下车动作识别网络/时间金字塔网络/违法载客/深度学习Key words
intelligent transportation/boarding and alighting action recognition/passenger boarding and alighting action recognition network/temporal pyramid network/illegal passenger carrying/deep learning分类
交通工程引用本文复制引用
廖惠敏,罗静茗,张璟辉,刘文平,董婉青,肖晖,黄坚..基于改进时间金字塔网络的出租车乘客上下车动作识别模型[J].交通信息与安全,2024,42(6):95-102,8.基金项目
国家重点研发计划项目(2022YFB2602104)、北京市交通行业科技项目(0686-2241B1251414Z)、车路一体智能交通全国重点实验室自主研究项目(2021-Z011)资助 （2022YFB2602104）
