Moving Object Detection Scheme for Automated Video Surveillance Systems

In every automated video surveillance system, moving object detection is an important pre-processing step leading to the extraction of useful information regarding moving objects present in a video scene. Most of the moving object detection algorithms require large memory space for storage of background related information which makes their implementation a difficult task on embedded platforms which are typically constrained by limited resources. Therefore, in order to overcome this limitation, in this paper we present a memory optimized moving object detection scheme for automated video surveillance systems with an objective to facilitate its implementation on standalone embedded platforms. The presented scheme is a modified version of the original clustering-based moving object detection algorithm and has been coded using C/C++ in the Microsoft Visual Studio IDE. The moving object detection results of the proposed memory efficient scheme were qualitatively and quantitatively analyzed and compared with the original clustering-based moving object detection algorithm. The experimental results revealed that there is 58.33% reduction in memory requirements in case of the presented memory efficient moving object detection scheme for storing background related information without any loss in accuracy and robustness as compared to the original clustering based scheme.