RAM: Role Representation and Identification from combined Appearance and Activity Maps
This work introduces a multimodal multiview camera network for role identication and re-identication in an Intensive Care Unit (ICU) room, where identifying individuals is not permitted. The analysis challenges include imaging conditions such as medical isolation (where all visitors wear scrubs), poor and non-uniform illumination, or variable camera views. We propose a role representation, which combines static appearance features such as texture and color, together with a dynamic quantification of human locations and interactions that results in a semantic map. The proposed representation is easy to compute and robust to varying ICU conditions and network configurations, which make the methods suitable for low-power distributed sensor network deployment. Thorough evaluations and comparisons with competing methods are performed. e ndings from this approach enable the compliant analysis of workflows in healthcare, while protecting the privacy of patients and medical staff.