Improved Change Detector Using Dual-Camera Sensors for Intelligent Surveillance Systems

Abstract

Unauthorized entrance in a prohibited area might create a security risk. An intelligent surveillance system should be able to mitigate such a problem by incorporating a sterile zone monitoring algorithm. The algorithm is challenged by a dual-camera sensors (color/IR), dynamic backgrounds, illumination changes, camouflaged, and static foreground objects, etc. This paper proposes an improved change detector (ICD) to mitigate the above-mentioned challenges. It employs a novel statistical decision criterion (SDC) based on skewness patterns. The SDC helps to differentiate time of day using the camera sensors (color/IR). The input frames are processed according to the time of day. For instance, IR input is image-enhanced to differentiate between camouflaged intruders from the background. Then input goes through Gaussian Mixture Models (GMM) based change detector to segment foreground (intruder). The foreground object is further cleansed using morphological operations for possible isolated noise and holes. The ICD was tested on three datasets and outperformed top-ranked change detection algorithms.