IECE Transactions on Emerging Topics in Artificial Intelligence Home About Editors Current Issue
-
CiteScore
3.50
Impact Factor
  1. Home
  2. Journals
  3. IECE Transactions on Emerging Topics in Artificial Intelligence
  4. Volume 1, Issue 1
CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
Research Article | Published: 29 May 2024
IECE Transactions on Emerging Topics in Artificial Intelligence Volume 1, Issue 1, 2024: 44-57
Volume 1, Issue 1, IECE Transactions on Emerging Topics in Artificial Intelligence
Volume 1, Issue 1, 2024
Submit Manuscript Edit a Special Issue
Academic Editor
Guoxiong Zhou
Guoxiong Zhou
Central South University of Forestry and Technology, China
Article QR Code
Article QR Code
Scan the QR code for reading
Popular articles
Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection Inaugural Editorial of the Chinese Journal of Information Fusion Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems YOLOv8-Lite: A Lightweight Object Detection Model for Real-time Autonomous Driving Systems Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform
IECE Transactions on Emerging Topics in Artificial Intelligence, Volume 1, Issue 1, 2024: 44-57

Open Access | Research Article | 29 May 2024
CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
by
Ming Gao Ming Gao 1 *
Shixin Yang Shixin Yang 2
1 School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
2 School of Computer Science and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
* Corresponding Author: Ming Gao, [email protected]
DOI: 10.62762/TETAI.2024.240529
Received: 23 November 2023, Accepted: 21 May 2024, Published: 29 May 2024  
Cited by: 2  (Source: Google Scholar)
   PDF  (13.97 MB) Full-Text HTML XML
Article Metrics Cite This Article
Abstract
In the era of rapid technological advancement, the demand for sophisticated Multi-Object Tracking (MOT) systems in applications such as intelligent surveillance and autonomous navigation has become increasingly critical. However, existing models often struggle with accuracy and efficiency in densely populated or dynamically complex environments. Addressing these challenges, we introduce a novel deep learning-based MOT model that incorporates the latest CT-DETR detection technology and an advanced ReID module for improved pedestrian tracking. Experimental results demonstrate the model's superior performance in accurately identifying and tracking multiple targets across varied scenarios, significantly outperforming existing benchmarks. This research not only marks a significant leap forward in the field of video surveillance technology but also lays a foundational framework for future advancements in intelligent system applications, underscoring the importance of innovation in deep learning methodologies for real-world challenges.
Graphical Abstract
CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes
Keywords
multi-object tracking
deep learning
CT-DETR
pedestrian re-identification
intelligent surveillance systems
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest. 
Ethical Approval and Consent to Participate
Not applicable.
References
  1. Wang, Y., Tang, C., Wang, S., Cheng, L., Wang, R., Tan, M., & Hou, Z. (2021). Target tracking control of a biomimetic underwater vehicle through deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 33(8), 3741-3752.
    [CrossRef]   [Google Scholar]
  2. Zhao, D., Cao, J., Zhu, X., Zhang, Z., Arun, P. V., Guo, Y., ... & Hu, J. (2022). Hyperspectral video target tracking based on deep edge convolution feature and improved context filter. Remote Sensing, 14(24), 6219. https://www.mdpi.com/2072-4292/14/24/6219
    [Google Scholar]
  3. Gao, S., Peng, Z., Liu, L., Wang, H., & Wang, D. (2021). Coordinated target tracking by multiple unmanned surface vehicles with communication delays based on a distributed event-triggered extended state observer. Ocean Engineering, 227, 108283.
    [CrossRef]   [Google Scholar]
  4. Wang, C., Wang, Y., Han, Y., Song, L., Quan, Z., Li, J., & Li, X. (2017, January). CNN-based object detection solutions for embedded heterogeneous multicore SoCs. In 2017 22nd Asia and South Pacific design automation conference (ASP-DAC) (pp. 105-110). IEEE.
    [CrossRef]   [Google Scholar]
  5. Li, T., Song, Y., & Fan, H. (2023). From target tracking to targeting track: A data-driven yet analytical approach to joint target detection and tracking. Signal Processing, 205, 108883.
    [CrossRef]   [Google Scholar]
  6. Ram, S. S. (2022). Fusion of inverse synthetic aperture radar and camera images for automotive target tracking. IEEE Journal of Selected Topics in Signal Processing.
    [CrossRef]   [Google Scholar]
  7. Chinthi-Reddy, S. R., Lim, S., Choi, G. S., Chae, J., & Pu, C. (2022). DarkSky: Privacy-preserving target tracking strategies using a flying drone. Vehicular Communications, 35, 100459.
    [CrossRef]   [Google Scholar]
  8. Liu, M., Wang, F., Wang, X., Wang, Y., & Roy-Chowdhury, A. K. (2024). A two-stage noise-tolerant paradigm for label corrupted person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(7), 4944-4956.
    [CrossRef]   [Google Scholar]
  9. Ning, E., Wang, C., Zhang, H., Ning, X., & Tiwari, P. (2023). Occluded person re-identification with deep learning: a survey and perspectives. Expert Systems with Applications, 122419.
    [CrossRef]   [Google Scholar]
  10. Wojke, N., Bewley, A., & Paulus, D. (2017, September). Simple online and realtime tracking with a deep association metric. In 2017 IEEE international conference on image processing (ICIP) (pp. 3645-3649). IEEE.
    [CrossRef]   [Google Scholar]
  11. Wu, D., Ye, M., Lin, G., Gao, X., & Shen, J. (2021). Person re-identification by context-aware part attention and multi-head collaborative learning. IEEE transactions on information forensics and security, 17, 115-126.
    [CrossRef]   [Google Scholar]
  12. Cui, Z., Zhou, J., Peng, Y., Zhang, S., & Wang, Y. (2023). Dcr-reid: Deep component reconstruction for cloth-changing person re-identification. IEEE transactions on circuits and systems for video technology, 33(8), 4415-4428.
    [CrossRef]   [Google Scholar]
  13. Sun, N., Zhao, J., Wang, G., Liu, C., Liu, P., Tang, X., & Han, J. (2022). Transformer-based moving target tracking method for Unmanned Aerial Vehicle. Engineering Applications of Artificial Intelligence, 116, 105483.
    [CrossRef]   [Google Scholar]
  14. Ebrahimi, M., Ardeshiri, M., & Khanghah, S. A. (2022). Bearing-only 2D maneuvering target tracking using smart interacting multiple model filter. Digital Signal Processing, 126, 103497.
    [CrossRef]   [Google Scholar]
  15. Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., ... & Wang, X. (2022, October). Bytetrack: Multi-object tracking by associating every detection box. In European conference on computer vision (pp. 1-21). Cham: Springer Nature Switzerland.
    [CrossRef]   [Google Scholar]
  16. Sun, Z., Chen, J., Chao, L., Ruan, W., & Mukherjee, M. (2020). A survey of multiple pedestrian tracking based on tracking-by-detection framework. IEEE Transactions on Circuits and Systems for Video Technology, 31(5), 1819-1833.
    [CrossRef]   [Google Scholar]
  17. Zhou, Q., Xia, H., Yan, H., Yang, M., & Chen, S. (2022, May). Adaptive multi-feature fusion visual target tracking based on Siamese neural network with cross-attention mechanism. In 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (pp. 307-316). IEEE.
    [CrossRef]   [Google Scholar]
  18. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
    [Google Scholar]
  19. Yang, J., Li, C., Zhang, P., Dai, X., Xiao, B., Yuan, L., & Gao, J. (2021). Focal attention for long-range interactions in vision transformers. Advances in Neural Information Processing Systems, 34, 30008-30022.
    [Google Scholar]
  20. Mayer, C., Danelljan, M., Paudel, D. P., & Van Gool, L. (2021). Learning target candidate association to keep track of what not to track. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 13444-13454).
    [CrossRef]   [Google Scholar]
  21. Dong, L., Xu, H., Feng, X., Han, X., & Yu, C. (2020). An adaptive target tracking algorithm based on EKF for AUV with unknown Non-Gaussian process noise. Applied Sciences, 10(10), 3413.
    [CrossRef]   [Google Scholar]
  22. Zhang, J., Hu, T., Shao, X., Xiao, M., Rong, Y., & Xiao, Z. (2021). Multi-target tracking using windowed Fourier single-pixel imaging. Sensors, 21(23), 7934.
    [CrossRef]   [Google Scholar]
  23. Cao, Z., Huang, Z., Pan, L., Zhang, S., Liu, Z., & Fu, C. (2023). Towards real-world visual tracking with temporal contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(12), 15834-15849.
    [CrossRef]   [Google Scholar]
  24. Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022). Transformers in vision: A survey. ACM computing surveys (CSUR), 54(10s), 1-41.
    [CrossRef]   [Google Scholar]
  25. Jin, X., Lan, C., Zeng, W., Wei, G., & Chen, Z. (2020, April). Semantics-aligned representation learning for person re-identification. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 11173-11180).
    [CrossRef]   [Google Scholar]
  26. Bewley, A., Ge, Z., Ott, L., Ramos, F., & Upcroft, B. (2016, September). Simple online and realtime tracking. In 2016 IEEE international conference on image processing (ICIP) (pp. 3464-3468). Ieee.
    [CrossRef]   [Google Scholar]
  27. Azhar, M. I. H., Zaman, F. H. K., Tahir, N. M., & Hashim, H. (2020, August). People tracking system using DeepSORT. In 2020 10th IEEE international conference on control system, computing and engineering (ICCSCE) (pp. 137-141). IEEE.
    [CrossRef]   [Google Scholar]
  28. Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., & Kim, T. K. (2021). Multiple object tracking: A literature review. Artificial intelligence, 293, 103448.
    [CrossRef]   [Google Scholar]
  29. Tan, L., Dong, X., Ma, Y., & Yu, C. (2018, October). A multiple object tracking algorithm based on YOLO detection. In 2018 11th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI) (pp. 1-5). IEEE.
    [CrossRef]   [Google Scholar]
  30. Kshirsagar, V., Bhalerao, R. H., & Chaturvedi, M. (2023). Modified yolo module for efficient object tracking in a video. IEEE Latin America Transactions, 21(3), 389-398.
    [CrossRef]   [Google Scholar]
  31. Milan, A., Leal-Taixé, L., Reid, I., Roth, S., & Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv preprint arXiv:1603.00831.
    [Google Scholar]
  32. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020, August). End-to-end object detection with transformers. In European conference on computer vision (pp. 213-229). Cham: Springer International Publishing.
    [CrossRef]   [Google Scholar]
  33. Foresti, G. L., & Pellegrino, F. A. (2004). Automatic visual recognition of deformable objects for grasping and manipulation. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(3), 325-333.
    [CrossRef]   [Google Scholar]
  34. Luo, W., Sun, P., Zhong, F., Liu, W., Zhang, T., & Wang, Y. (2019). End-to-end active object tracking and its real-world deployment via reinforcement learning. IEEE transactions on pattern analysis and machine intelligence, 42(6), 1317-1332.
    [CrossRef]   [Google Scholar]
  35. Sun, S., Wang, Y., & Piao, Y. (2021, May). A Real-time Multi-target tracking method based on Deep Learning. In Journal of Physics: Conference Series (Vol. 1920, No. 1, p. 012112). IOP Publishing.
    [CrossRef]   [Google Scholar]
  36. Chen, X., Li, D., & Zou, Q. (2021). Exploiting Acceleration of the Target for Visual Object Tracking. IEEE Access, 9, 73818-73825.
    [CrossRef]   [Google Scholar]
  37. Zhao, F., Hui, K., Wang, T., Zhang, Z., & Chen, Y. (2021). A KCF-based incremental target tracking method with constant update speed. IEEE Access, 9, 73544-73560.
    [CrossRef]   [Google Scholar]
  38. Jia, X., Lu, H., & Yang, M. H. (2012, June). Visual tracking via adaptive structural local sparse appearance model. In 2012 IEEE Conference on computer vision and pattern recognition (pp. 1822-1829). IEEE.
    [CrossRef]   [Google Scholar]
  39. Yang, X., Zhu, S., Xia, S., & Zhou, D. (2020). A new TLD target tracking method based on improved correlation filter and adaptive scale. The Visual Computer, 36(9), 1783-1795.
    [CrossRef]   [Google Scholar]
  40. Duan, Y., Wu, W., Liu, L., Liu, S., Liang, P., & Zhang, Y. (2022, December). DTTrack: Target Tracking Algorithm Combining DaSiamRPN Tracker and Transformer Tracker. In Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence (pp. 1-5).
    [CrossRef]   [Google Scholar]
  41. Qian, K., Zhang, S. J., Ma, H. Y., & Sun, W. J. (2023). SiamIST: Infrared small target tracking based on an improved SiamRPN. Infrared Physics & Technology, 134, 104920.
    [CrossRef]   [Google Scholar]
Cite This Article
APA Style
Gao, M., & Yang, S. (2024). CT-DETR and ReID-Guided Multi-Target Tracking Algorithm in Complex Scenes. IECE Transactions on Emerging Topics in Artificial Intelligence, 1(1), 44–57. https://doi.org/10.62762/TETAI.2024.240529
Article Metrics
Citations:

Google Scholar
2

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 1164
PDF Downloads: 245
Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
CC BY Copyright © 2024 by the Author(s). Published by Institute of Emerging and Computer Engineers. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
IECE Transactions on Emerging Topics in Artificial Intelligence

IECE Transactions on Emerging Topics in Artificial Intelligence

ISSN: 3066-1676 (Online) | ISSN: 3066-1668 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.