Image Representation for Cognitive Systems Using SOEKS and DDNA: A Case Study for PPE Compliance

Abstract

Cognitive Vision Systems have gained significant interest from academia and industry during the past few decade, and one of the main reasons behind this is the potential of such technologies to revolutionize human life as they intend to work under complex visual scenes, adapting to a comprehensive range of unforeseen changes, and exhibiting prospective behavior. The combination of these properties aims to mimic the human capabilities and create more intelligent and efficient environments. Nevertheless, preserving the environment such as humans do still remains a challenge in cognitive systems applications due to the complexity of such process. Experts believe the starting point towards real cognitive vision systems is to establish a representation which could integrate image/video modularization and virtualization, together with information from other sources (wearable sensors, machine signals, context, etc.) and capture its knowledge. In this paper we show through a case study how Decisional DNA (DDNA), a multi-domain knowledge structure that has the Set of Experience Knowledge Structure (SOEKS) as its basis can be utilized as a comprehensive embedded knowledge representation in a Cognitive Vision System for Hazard Control (CVP-HC). The proposed application aims to ensure that workers remain safe and compliant with Health and Safety policy for use of Personal Protective Equipment (PPE) and serves as a showcase to demonstrate the representation of visual and non-visual content together as an experiential knowledge in one single structure.