Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence
As a vital technology in Cyber-Physical Social Intelligence (CPSI), Multi-Object-Tracking (MOT) can support comprehensive perception and analysis of the physical environment and social virtual space, promoting an in-depth understanding of human behavior, object movement, and social interaction. Most...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2025-06-01
|
| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2025.9020024 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850067579038597120 |
|---|---|
| author | Jieming Yang Dezhen Feng Yuan Gao Cong Liu |
| author_facet | Jieming Yang Dezhen Feng Yuan Gao Cong Liu |
| author_sort | Jieming Yang |
| collection | DOAJ |
| description | As a vital technology in Cyber-Physical Social Intelligence (CPSI), Multi-Object-Tracking (MOT) can support comprehensive perception and analysis of the physical environment and social virtual space, promoting an in-depth understanding of human behavior, object movement, and social interaction. Most MOT methods often adopt simple interpolation or prediction strategies when dealing with temporarily lost targets, but ignore the comprehensive consideration of the state of the target before its reappearance. This approach may lead to an incomplete understanding of the target’s behavior and dynamics, which affects the accuracy and depth of the comprehensive understanding of social and physical space interactions in the real world. To improve it, we propose an online multi-object tracking method based on Record Confidence and Hierarchical Association (RCHA), which is represented as RCHA-Track. The Kalman filter combined with an Enhanced Correlation Coefficient (ECC) provides more accurate motion prediction under the influence of camera motion. The record confidence is designed to evaluate the loss status of the unseen object and refine the tracking trajectory. The normally tracked targets and the temporarily lost targets are combined to perform a hierarchical association based on the number of lost frames to achieve more accurate data associations. Compared with the latest ByteTrack, RCHA-Track improves MOTA, IDF1, and HOTA by 1.7%, 1.6%, and 1.3% on the benchmark dataset MOT17, and 1.3%, 2.1%, and 2.0% on MOT20, respectively, achieving state-of-the-art performance. Extensive ablation experiments demonstrate the effectiveness of each key module in the proposed RCHA-Track. |
| format | Article |
| id | doaj-art-a71b22cc101e4da28256d644d02d65f2 |
| institution | DOAJ |
| issn | 2096-0654 2097-406X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-a71b22cc101e4da28256d644d02d65f22025-08-20T02:48:16ZengTsinghua University PressBig Data Mining and Analytics2096-06542097-406X2025-06-018485186610.26599/BDMA.2025.9020024Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social IntelligenceJieming Yang0Dezhen Feng1Yuan Gao2Cong Liu3School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Computer Science and technology, Hainan University, Haikou 570228, ChinaSchool of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, ChinaSchool of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, ChinaAs a vital technology in Cyber-Physical Social Intelligence (CPSI), Multi-Object-Tracking (MOT) can support comprehensive perception and analysis of the physical environment and social virtual space, promoting an in-depth understanding of human behavior, object movement, and social interaction. Most MOT methods often adopt simple interpolation or prediction strategies when dealing with temporarily lost targets, but ignore the comprehensive consideration of the state of the target before its reappearance. This approach may lead to an incomplete understanding of the target’s behavior and dynamics, which affects the accuracy and depth of the comprehensive understanding of social and physical space interactions in the real world. To improve it, we propose an online multi-object tracking method based on Record Confidence and Hierarchical Association (RCHA), which is represented as RCHA-Track. The Kalman filter combined with an Enhanced Correlation Coefficient (ECC) provides more accurate motion prediction under the influence of camera motion. The record confidence is designed to evaluate the loss status of the unseen object and refine the tracking trajectory. The normally tracked targets and the temporarily lost targets are combined to perform a hierarchical association based on the number of lost frames to achieve more accurate data associations. Compared with the latest ByteTrack, RCHA-Track improves MOTA, IDF1, and HOTA by 1.7%, 1.6%, and 1.3% on the benchmark dataset MOT17, and 1.3%, 2.1%, and 2.0% on MOT20, respectively, achieving state-of-the-art performance. Extensive ablation experiments demonstrate the effectiveness of each key module in the proposed RCHA-Track.https://www.sciopen.com/article/10.26599/BDMA.2025.9020024multi-object trackingdeep learningobject detectionneural network |
| spellingShingle | Jieming Yang Dezhen Feng Yuan Gao Cong Liu Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence Big Data Mining and Analytics multi-object tracking deep learning object detection neural network |
| title | Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence |
| title_full | Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence |
| title_fullStr | Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence |
| title_full_unstemmed | Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence |
| title_short | Online Multi-Object Tracking Based on Record Confidence and Hierarchical Association for Cyber-Physical Social Intelligence |
| title_sort | online multi object tracking based on record confidence and hierarchical association for cyber physical social intelligence |
| topic | multi-object tracking deep learning object detection neural network |
| url | https://www.sciopen.com/article/10.26599/BDMA.2025.9020024 |
| work_keys_str_mv | AT jiemingyang onlinemultiobjecttrackingbasedonrecordconfidenceandhierarchicalassociationforcyberphysicalsocialintelligence AT dezhenfeng onlinemultiobjecttrackingbasedonrecordconfidenceandhierarchicalassociationforcyberphysicalsocialintelligence AT yuangao onlinemultiobjecttrackingbasedonrecordconfidenceandhierarchicalassociationforcyberphysicalsocialintelligence AT congliu onlinemultiobjecttrackingbasedonrecordconfidenceandhierarchicalassociationforcyberphysicalsocialintelligence |