MSCPT: Toward Cross-Place Transportation Mode Recognition Based on Multi-Sensor Neural Network Model

doi:10.1109/TITS.2021.3115264

	MSCPT: Toward Cross-Place Transportation Mode Recognition Based on Multi-Sensor Neural Network Model
	Zhu, Yida 1; Luo, Haiyong 2; Chen, Runze 1; Zhao, Fang 1; Guo, Song 1
	2021-10-01
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
页码	13
摘要	With the ever-increasing intelligent perception capability of mobile terminals, the demand for fine-grained human activity recognition has become urgent. Transportation mode recognition, as a special branch of human activity recognition, plays a vital role in various mobile smart services. Some studies have been conducted on attitude-independence transportation mode recognition from various perspectives. However, it is challenging to recognize cross-place transportation mode due to the diversity of human activity and carrier deployment place. Towards this end, we propose a robust cross-place transportation mode recognition algorithm, which consists of three parts: Multi-Sensor Neural Network model, a variant bootstrap (ensemble learning) method, and data augmentation. The Multi-Sensor Neural Network model leverages multiple SF-SEDNets to extract spatial-temporal and spectral fusion features from different sensor combinations. The proposed data augmentation method addresses the unbalance data problem without introducing additional data collection costs, and the variant bootstrap method improves the robustness of our proposed algorithm. We evaluate our proposed algorithm on the Sussex-Huawei Locomotion-Transportation recognition challenge 2019 dataset. Extensive experimental results indicate that our algorithm achieves 81.45% macro-F1 score on the test dataset, which is 3.03% higher than that provided by the winning entry of this competition and is 14.85% higher than that of the official baseline.
关键词	Transportation Feature extraction Data models Navigation Computational modeling Bagging Accelerometers Cross-place transportation mode recognition SF-SEDNet global attention ensemble learning data augmentation
DOI	10.1109/TITS.2021.3115264
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2018YFB0505200] ; Action Plan Project of Beijing University of Posts and Telecommunications through the Fundamental Research Funds for the Central Universities[2019XD-A06] ; National Natural Science Foundation of China[61872046] ; National Natural Science Foundation of China[62002026] ; Joint Research Fund for Beijing Natural Science Foundation[L192004] ; Haidian Original Innovation[L192004] ; Beijing Natural Science Foundation[4212024] ; Key Research and Development Project from Hebei Province[19210404D] ; Science and Technology Plan Project of Inner Mongolia Autonomous Region[2019GG328] ; BUPT Excellent Ph.D. Students Foundation[CX2020220] ; Open Project of Beijing Key Laboratory of Mobile Computing and Pervasive Device
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000732367500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17960
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Luo, Haiyong; Zhao, Fang
作者单位	1.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing 100081, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Yida,Luo, Haiyong,Chen, Runze,et al. MSCPT: Toward Cross-Place Transportation Mode Recognition Based on Multi-Sensor Neural Network Model[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021:13.
APA	Zhu, Yida,Luo, Haiyong,Chen, Runze,Zhao, Fang,&Guo, Song.(2021).MSCPT: Toward Cross-Place Transportation Mode Recognition Based on Multi-Sensor Neural Network Model.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA	Zhu, Yida,et al."MSCPT: Toward Cross-Place Transportation Mode Recognition Based on Multi-Sensor Neural Network Model".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021):13.