MAPS: A Morphology-Aware PPE Segmentation Framework for Healthcare Settings

Wanzhao Yang, Syed Anwar, Beomseok Park, Sifan Yuan, Aleksandra Sarcevic, Marius G. Linguraru, Randall S. Burd, Ivan Marsic; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 4442-4450

Monitoring adherence to personal protective equipment (PPE) guidelines is critical for infection control in clinical environments. Automated methods for monitoring require precise localization of PPE in complex real-world videos. While recent video segmentation models like SAM2 have shown strong performance, they underperform in healthcare settings due to cluttered backgrounds and frequent occlusions of small PPE items such as masks and gloves. We have identified two core limitations of SAM2 in this context: (1) difficulty in distinguishing PPE objects from complex backgrounds, and (2) tracking drift during occlusion. To address these issues, we propose MAPS: Morphology Aware PPE Segmentation, a training-free extension of SAM2 that incorporates two novel components: (1) a morphology-aware memory module that leverages shape descriptors to selectively retain reliable memory features and (2) a person-aware filtering module that removes predictions that do not align with detected person regions. MAPS achieves consistent improvements across multiple SAM2 model scales and outperforms recent SAM2-based extensions on a newly introduced PPE object tracking dataset. The code and the new dataset are available at https://github.com/yangwanzhao/MAPS.