交通信息与安全2025，Vol.43Issue(1)：120-129,140,11.DOI:10.3963/j.jssn.1674-4861.2025.01.011
基于改进TSM的船舶驾驶员行为识别方法
A Novel Ship Driver Behavior Recognition Approach Based on Improved TSM
摘要
Abstract
In maritime transportation,irregular operations by crew onboard represent a significant factor causing maritime accidents.The design of a real-time detection method for monitoring ship driver behavior holds substan-tial importance.Compared to automobilism driving and security surveillance,the ship's bridge environment is more complex,posing challenges such as the inability to simultaneously monitor multiple crew members,ineffi-ciency and lower accuracy rates.To solve this problem,a two-step multi-person behavior recognition approach combining multi-target tracking and behavior recognition is proposed.Firstly,a multi-target tracker uses the Yo-loV7 and ByteTracker to generate continuous feature maps of crew.Based on the temporal shift module(TSM)al-gorithm for single-target behavior recognition,this approach utilizes techniques such as oversampling and cross-frame stitching to process continuous feature maps.Meanwhile,it leverages EfficientNet-B3 alongside the co-ordinate attention(CA)module to produce highly accurate recognition outcomes.The research establishes a ship's bridge behavior dataset"SC-Action",with data from different ship's bridge surveillance videos,including 2,000 be-havior samples of both regular and irregular behaviors.Transfer learning and ablation experiments conducted on this dataset demonstrate that the proposed method achieves real-time behavior recognition of three crew at 24 frames per second,with both recognition speed and accuracy superior to mainstream algorithms.In tests targeting single-person behavior recognition,the method's accuracy improved by 1.3%compared to the baseline TSM model after applying the image enhancement module.Incorporating attention mechanism,the accuracy further increased by 1.78%,reaching 82.1%,with only a 0.1%increase in computational load.During multi-target testing,the meth-od also surpasses leading approaches such as SlowFast in practical inference speed and performance,affirming its efficacy.关键词
航行安全/行为识别/目标跟踪/注意力机制/temporal shift moduleKey words
navigation safety/behavior recognition/target tracking/attention mechanism/temporal shift module分类
交通工程引用本文复制引用
陈晨,魏月楠,马枫,胡松涛,王腾飞..基于改进TSM的船舶驾驶员行为识别方法[J].交通信息与安全,2025,43(1):120-129,140,11.基金项目
国家自然科学基金项目(52201415、52171352)、国家重点研发计划项目(2023YFB4302300)、水路交通控制全国重点实验室开放课题项目(16-10-1)资助 （52201415、52171352）
