Citation

Khan, Said Baz Jahfar and Zhang, Peng and Kamal, Mian Muhammad and Alharbi, Abdullah G. and Tolba, Amr and Sheraz, Muhammad and Chuah, Teong Chee (2026) TraceNet: A novel modular framework for robust Multi-Object Tracking in crowded and dynamic environments. Alexandria Engineering Journal, 137. pp. 401-413. ISSN 11100168

Text TraceNet_ A novel modular framework for robust Multi-Object Tracking in crowded and dynamic environments.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Multi-Object Tracking (MOT) is a fundamental task in computer vision, vital for applications in autonomous driving, intelligent surveillance, and sports data analysis. However, tracking performance significantly degrades under conditions such as occlusion, small object instances, and fast motion. This work proposes TraceNet, a modular multi-object tracking framework designed to address these challenges by incorporating sophisticated detection, association, and recovery components. TraceNet builds on a fine-tuned YOLOv11 detector and incorporates a Confidence Optimization Network (CON) to improve detection reliability in low-visibility environments. It further includes a Deep Similarity Integration (DSI) module improved by Dynamic IoU Adjustment (DIA), which combines motion prediction and appearance cues to achieve reliable identification associations. The framework uses a Graph-Based Track Recovery (GBTR) network and a Neural Trajectory Smoother (NTS) to recover interrupted trajectories and ensure temporal consistency. The temporal association is further enhanced by a Transformer-Based Association (TBA) module. TraceNet achieves exceptional performance on four challenging benchmarks, achieving HOTA scores of 66.9 for MOT17 and 66.7 for MOT20, with IDF1 scores of 83.2 and 83.5, respectively. These results highlight TraceNet’s robustness in dense and occluded scenes, and demonstrate that it is a high-performing and scalable solution for real-time multi-object tracking.

Item Type:	Article
Uncontrolled Keywords:	Computer vision
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Mar 2026 00:22
Last Modified:	02 Mar 2026 00:22
URII:	http://shdl.mmu.edu.my/id/eprint/15378

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