Deriving implicit indoor scene structure with path analysis

Abstract

Indoor video surveillance is now widely used in government, public, and private facilities. While the capacity to generate such video data is increasing, our ability to derive a coherent scene understanding of the structure of the scene and how it is being utilized, using only motion data, is still lagging behind. This paper proposes a framework for deriving indoor scene structure identifying abnormal motion behavior using only video tracking data, and without requiring a floor plan. The proposed framework, which is data-driven, is based on four sequential processing steps, namely detection of entrance and exit points, the analysis of the connectivity between entrance and exit points, the extraction of mean paths and motion corridors, and the statistical analysis of the length and velocity parameters of motion for the detection of abnormal motion behavior. The paper outlines the proposed framework and demonstrates its implementation using a real-world data set comprising 1138 trajectories.