Interior Visual Intruders Detection Module Based on Multi-Connect Architecture MCA Associative Memory

Abstract

Most recent studies have focused on using modern intelligent techniques spatially, such as those developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the ability to learn and recognize what they had learned. The importance of developing such systems came after the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder detection module depending on Multi-Connect Architecture Associative Memory (MCA) has been proposed. Via using the MCA associative memory as a new trend, the proposed module goes through two phases: the first is the training phase (which is executed once during the module installation process) and the second is the analysis phase. Both phases will be developed through the use of MCA, each according to its process. The training phase will take place through the learning phase of MCA, while the analysis phase will take place through the convergence phase of MCA. The use of MCA increases the efficiency of the training process for the proposed system by using a minimum number of training images that do not exceed 10 training images of the total number of frames in JPG format. The proposed module has been evaluated using 11,825 images that have been extracted from 11 tested videos. As a result, the module can detect the intruder with an accuracy ratio in the range of 97%–100%. The average training process time for the training videos was in the range of 10.2 s to 23.2 s.