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Multi-Stream Siamese and Faster Region-Based Neural Network for Real-Time Object Tracking
Liu, Yi1; Zhang, Liming2; Chen, Zhihui1; Yan, Yan2; Wang, Hanzi1
2021-11-01
Source PublicationIEEE Transactions on Intelligent Transportation Systems
ISSN1524-9050
Volume22Issue:11Pages:7279-7292
Abstract

Object tracking is a challenging task in computer vision based intelligent transportation systems. Recently, Siamese based object tracking methods have attracted significant attention due to their highly efficient performance. These tracking methods usually train a Siamese network to match the initial target patch of the first frame with candidates in a new frame. In these methods, the offline training of the deep neural network and the online instance searching are effectively combined. However, these methods usually do not include template update or object re-identification, which easily results in the drift problem. In this paper, we propose a novel real-time object tracking method to overcome the above problems by effectively combining a multi-stream Siamese network and a region-based convolutional neural network. Specifically, a novel multi-stream Siamese network is proposed to search the target and update the instance template in a new frame. In addition, a faster region-based convolutional neural network detector is used to perform object re-identification in order to improve the tracking performance by making full use of the object category information. These two networks are tightly coupled to ensure that the proposed tracking method has high efficiency and strong discriminative capability. Experimental results on several object tracking benchmarks show that our tracking method can effectively track vehicles and pedestrians in video sequences by exploiting the object category information. The proposed tracking method achieves real-time operations and outperforms several other state-of-the-art methods.

KeywordFaster R-cnn Instance Search Multi-stream Siamese Network Object Tracking
DOI10.1109/TITS.2020.3006927
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000714240200053
Scopus ID2-s2.0-85118874923
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Citation statistics
Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorWang, Hanzi
Affiliation1.Fujian Key Laboratory of Sensing and Computing for Smart City, School of Informatics, Xiamen University, Xiamen, China
2.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Taipa, Macao
Recommended Citation
GB/T 7714
Liu, Yi,Zhang, Liming,Chen, Zhihui,et al. Multi-Stream Siamese and Faster Region-Based Neural Network for Real-Time Object Tracking[J]. IEEE Transactions on Intelligent Transportation Systems,2021,22(11):7279-7292.
APA Liu, Yi,Zhang, Liming,Chen, Zhihui,Yan, Yan,&Wang, Hanzi.(2021).Multi-Stream Siamese and Faster Region-Based Neural Network for Real-Time Object Tracking.IEEE Transactions on Intelligent Transportation Systems,22(11),7279-7292.
MLA Liu, Yi,et al."Multi-Stream Siamese and Faster Region-Based Neural Network for Real-Time Object Tracking".IEEE Transactions on Intelligent Transportation Systems 22.11(2021):7279-7292.
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