@article{Hu2026, 
author = {Qin Hu and Hongshan Kong},
title = {Face-Pedestrian Joint Feature Modeling with Cross-Category Dynamic Matching for Occlusion-Robust Multi-Object Tracking},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {86},
number = {1},
pages = {1-31},
keywords = {Cross-category dynamic binding, joint feature modeling, face-pedestrian association, multi object tracking, occlusion robustness},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.069078},
doi = {10.32604/cmc.2025.069078},
abstract = {To address the issues of frequent identity switches (IDs) and degraded identification accuracy in multi object tracking (MOT) under complex occlusion scenarios, this study proposes an occlusion-robust tracking framework based on face-pedestrian joint feature modeling. By constructing a joint tracking model centered on “intra-class independent tracking + cross-category dynamic binding”, designing a multi-modal matching metric with spatio-temporal and appearance constraints, and innovatively introducing a cross-category feature mutual verification mechanism and a dual matching strategy, this work effectively resolves performance degradation in traditional single-category tracking methods caused by short-term occlusion, cross-camera tracking, and crowded environments. Experiments on the Chokepoint_Face_Pedestrian_Track test set demonstrate that in complex scenes, the proposed method improves Face-Pedestrian Matching F1 area under the curve (F1 AUC) by approximately 4 to 43 percentage points compared to several traditional methods. The joint tracking model achieves overall performance metrics of IDF1: 85.1825% and MOTA: 86.5956%, representing improvements of 0.91 and 0.06 percentage points, respectively, over the baseline model. Ablation studies confirm the effectiveness of key modules such as the Intersection over Area (IoA)/Intersection over Union (IoU) joint metric and dynamic threshold adjustment, validating the significant role of the cross-category identity matching mechanism in enhancing tracking stability. Our_model shows a 16.7% frame per second (FPS) drop vs. fairness of detection and re-identification in multiple object tracking (FairMOT), with its cross-category binding module adding aboute 10% overhead, yet maintains near-real-time performance for essential face-pedestrian tracking at small resolutions.}
}