A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location
In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/5/2408 |
| _version_ | 1827752259758325760 |
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| author | Yiru Jiang Dezhi Han Mingming Cui Yuan Fan Yachao Zhou |
| author_facet | Yiru Jiang Dezhi Han Mingming Cui Yuan Fan Yachao Zhou |
| author_sort | Yiru Jiang |
| collection | DOAJ |
| description | In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis. |
| first_indexed | 2024-03-11T07:11:59Z |
| format | Article |
| id | doaj.art-e372102bb47246689a40075b308c99e5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T07:11:59Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e372102bb47246689a40075b308c99e52023-11-17T08:34:20ZengMDPI AGSensors1424-82202023-02-01235240810.3390/s23052408A Video Target Tracking and Correction Model with Blockchain and Robust Feature LocationYiru Jiang0Dezhi Han1Mingming Cui2Yuan Fan3Yachao Zhou4College of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaCollege of Information Engineering, Shanghai Maritime University, Shanghai 201306, ChinaHangzhou Anheng Information Technology Co., Ltd., Hangzhou 310051, ChinaHangzhou Anheng Information Technology Co., Ltd., Hangzhou 310051, ChinaIn this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.https://www.mdpi.com/1424-8220/23/5/2408decentralized surveillancesecure tracking of objectsprivacy-preserving target trackingtarget correctiontrajectory optimization |
| spellingShingle | Yiru Jiang Dezhi Han Mingming Cui Yuan Fan Yachao Zhou A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location Sensors decentralized surveillance secure tracking of objects privacy-preserving target tracking target correction trajectory optimization |
| title | A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location |
| title_full | A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location |
| title_fullStr | A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location |
| title_full_unstemmed | A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location |
| title_short | A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location |
| title_sort | video target tracking and correction model with blockchain and robust feature location |
| topic | decentralized surveillance secure tracking of objects privacy-preserving target tracking target correction trajectory optimization |
| url | https://www.mdpi.com/1424-8220/23/5/2408 |
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