Monitoring a Crowd's Affective State: Status Quo and Future Outlook

Abstract

Crowd monitoring plays an integral role in crowd management processes and continues to receive growing interest across different public and commercial service sectors. In recent years, synergistic advances in different domains, such as computing, sensing, IoT, drones, and AI/ML, have empowered monitoring architectures with various enhancements in identifying patterns in crowd dynamics. However, efforts to examine affect in crowd monitoring considerations have been lacking in the current literature. In this work, we address this void by reviewing the recent advances and enabling technologies in affective sensing at both the individual and crowd levels. We also remark on deployment considerations in affective sensing architectures and key outstanding challenges.