CALIBFREE: Self-Supervised Feature Disentanglement for Calibration-Free Multi-Camera Multi-Object Tracking

Publication Date: 5/12/2025

Event: arXiv

Reference: https://arxiv.org/abs/2605.09245v1

Authors: Ruiqi Xian, NEC Laboratories America, Inc., University of Maryland; Iain Melvin, NEC Laboratories America, Inc.; Deep Patel, NEC Laboratories America, Inc.; Sanjoy Kundu, NEC Laboratories America, Inc., Auburn University; Martin Renqiang Min, NEC Laboratories America, Inc.; Dinesh Manocha, University of Maryland

Abstract: Multi-camera multi-object tracking (MCMOT) faces significant challenges in maintaining consistent object identities across varying camera perspectives, particularly when precise calibration and extensive annotations are required. In this paper, we present CalibFree, a self-supervised representation learning framework that does not need any calibration or manual labeling for the MCMOT task. By promoting feature separation between view-agnostic and view-specific representations through single-view distillation and cross-view reconstruction, our method adapts to complex, dynamic scenarios with minimal overhead. Experiments on the MMP-MvMHAT dataset show a 3% improvement in overall accuracy and a 7.5% increase in the average F1 score over state-of-the-art approaches, confirming the effectiveness of our calibration-free design. Moreover, on the more diverse MvMHAT dataset, our approach demonstrates superior over-time tracking and strong cross-view performance, highlighting its adaptability to a wide range of camera configurations. Code will be publicly available upon acceptance.

Publication Link: https://arxiv.org/abs/2605.09245v1