Using Explainable AI to Predict Crowd Dynamics From Video Sequences
Gokul Raj Rajasekaran, A Thenmozhi, A. T. Ashmi Christus, B. Sheeba, C. Satheesh, Ravi Kumar Batchu · Advances in computer and electrical engineering book series · 2024
Understanding and predicting crowd dynamics from video sequences is a critical task with applications spanning public safety, event management, and urban planning. The chapter introduces a groundbreaking approach that harnesses the power of Explainable AI (XAI) to not only predict but also comprehensively understand crowd dynamics derived from video data. Predicting crowd behavior accurately is challenging, but providing interpretable insights into the reasoning behind these predictions is equally essential. The methodology achieves both of these objectives. By leveraging XAI techniques, the approach yields highly accurate predictions of crowd dynamics while simultaneously demystifying the underlying logic of these predictions. The significance of this approach lies in its ability to provide an understandable rationale behind complex AI decisions. Stakeholders, including law enforcement, event organizers, and urban planners, can benefit from this level of transparency. It enables them to make informed decisions based on AI analyses and contributes to the safety and efficiency of public spaces and events. The incorporation of XAI into crowd dynamics prediction not only enhances prediction accuracy but also builds trust among end-users. It bridges a significant gap in the field by ensuring that AI is not just a black box making predictions but a valuable tool that can be reasoned with and trusted. This research offers a transformative approach to crowd dynamics prediction, where the power of AI is harnessed, and the rationale behind its predictions is made accessible to stakeholders. This approach is poised to have a profound impact on the fields of public safety, event management, and urban planning, ensuring that AI contributes positively to the well-being of communities.