Multi-camera Background and Scene Activity Modelling Based on Spearman Correlation Analysis and Inception-V3 Network

A novel approach for background and scene activity modelling with spearman correlation analysis and customized deep learning model is introduced in this paper. It detects and gives correlated analytics between casual and temporal regional activities on the basis of similarities and primary dissimilarities in the same scene captured by several cameras. The experiment implement on four overlapped videos that are captured inside the hall from four cameras. Detected and analyzed by our model, 17.32% correlated co-occurrences is actual correlation among all videos. Rest of 82.68% of videos is background that shows similar and repetitive features in spearman rank tied result. Simulation results demonstrate that the proposed method can detect high correlation among all activities during the frame rate with tied features ability.