Deep Learning‐Driven Intrusion Prediction System Using Ground‐Plane Homography in Smart City Dynamic Zones

ABSTRACT In smart city environments, public safety increasingly depends on intelligent surveillance systems that can be capable of adapting to dynamic and context‐dependent access restrictions. Traditional systems often rely on static and predefined boundaries that fail to respond to rapidly changing environments such as construction sites, public gatherings or emergency situations. This paper introduces a novel deep learning‐driven framework using ground‐plane homography for real‐time proactive intrusion prediction within these dynamically restricted zones (DRZs). Our method first employs deep learning to accurately detect and localise physical restriction markers (e.g., traffic cones). We then utilise ground‐plane homography estimation to accurately map these markers into two‐dimensional ground‐plane perspective, precisely defining the spatial boundaries of the DRZ in real‐time. After the reactive detection of restriction markers region, intrusion prediction is achieved through sophisticated human trajectory analysis and future path extrapolation. By forecasting a person's path and identifying projected future presence within the dynamic ground‐plane zone, the system assists proactive alerts and adaptive security responses before an actual violation. To the best of our knowledge, this is the first system capable of predicting intrusions into areas dynamically demarcated by visual restriction markers. The experimental results on real‐world surveillance datasets demonstrate the system's effectiveness in identifying the presence of humans in DRZ, validating its potential for deployment in smart cities and critical infrastructure.