This paper proposes a privacy preserving data mining driven methodology for predicting emerging human threats in a public space by capturing large scale, real time body movement data (spatial data represented in X, Y, Z coordinate space) using Red-Green-Blue (RGB) image, infrared depth and skeletal image sensing technology. Unlike traditional passive surveillance systems (e.g., CCTV video surveillance systems), multimodal surveillance technologies have the ability to capture multiple data streams in a real time dynamic manner. However, mathematical models based on machine learning principles are needed to convert the large-scale data into knowledge to serve as a decision support system for autonomously predicting emerging threats, rather than just recording and observing them as they occur.
To this end, the authors of this work present a privacy preserving data mining driven methodology that captures emergent behavior of individuals in a public space and classifies them as a threat or not a threat, based on the underlying body movements through space and time. An audience in a public environment is presented as the case study for this paper with the aim of classifying individuals in the audience as threats (or not), based on their temporal body behavior profiles.
Hierarchical Infrastructure for Large-Scale Distributed Privacy-Preserving Data Mining
