Interior Visual Intruders Detection Module Based on Multi-Connect Architecture MCA Associative Memory
Keywords:Associative memory, Interior IDS, Intruders Detection System IDS, MCA Associative Memory
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.
Published Online First 20/9/2022
Vincent M, Arumugam P, Prabhakara Rao G. Interior intrusion detection system. Proc - Int Carnahan Conf Secur Technol. 2019; 2019-Octob: 2–5.
Gong L, Yang W, Zhou Z, Man D, Cai H, Zhou X, et al. An adaptive wireless passive human detection via fine-grained physical layer information. Ad Hoc Networks [Internet]. 2016; 38: 38–50. Available from: http://dx.doi.org/10.1016/j.adhoc.2015.09.005
Ko S, Yu S, Kang W, Park C, Lee S, Paik J. Artifact-Free Low-Light Video Enhancement Using Temporal Similarity and Guide Map. IEEE Trans Ind Electron. 2017; 64(8): 6392–401.
Park S, Moon B, Ko S, Yu S, Paik J. Low-light image restoration using bright channel prior-based variational Retinex model. Eurasip J Image Video Process. 2017; 2017(1): 1–11.
Castro JL, Delgado M, Medina J, Ruiz-Lozano MD. Intelligent surveillance system with integration of heterogeneous information for intrusion detection. Expert Syst Appl [Internet]. 2011; 38(9): 11182–92. Available from: http://dx.doi.org/10.1016/j.eswa.2011.02.165
Wu D, Zhang D, Xu C, Wang H, Li X. Device-Free WiFi Human Sensing: From Pattern-Based to Model-Based Approaches. IEEE Commun Mag. 2017; 55(10): 91–7.
Liu H, Lang B. Machine learning and deep learning methods for intrusion detection systems: A survey. Appl Sci. 2019; 9(20).
Noor Adnan Ibraheem. Modifying Hebbian Network for Text Cipher. Baghdad Sci J [Internet]. 2011 Dec 4; 8(4 SE-): 1028–37. Available from: https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/1325
Hussien Z K, Dahannon B N. Anomaly Detection Approach Based on Deep Neural Network and Dropout. Baghdad Sci J. https://doi.org/10.21123/bsj.2020.17.2(SI).0701
Emad I Abdul Kareem, Aman Jantan. MCA: A Developed Associative Memory Using Multi-Connect Architecture. Int J Intell Inf Process. 2011; 2(1): 49–62.
Emad I Abdul Kareem, Wafaa A.H. Ali Alsalihy, Aman Jantan. Multi-Connect Architecture (MCA) Associative Memory: A Modified Hopfield Neural Network. J Intell Autom Soft Comput. 2012; 18(3): 291–308.
Palipana S, Agrawal P, Pesch D. Channel state information based human presence detection using non-linear techniques. Proc 3rd ACM Conf Syst Energy-Efficient Built Environ BuildSys 2016. 2016; 177–86.
Qian K, Wu C, Yang Z, Liu Y, Fugui HE, Xing T. Enabling contactless detection of moving humans with dynamic speeds using CSI. ACM Trans Embed Comput Syst. 2018; 17(2): 1–18.
Soltanaghaei E, Kalyanaraman A, Whitehouse K. Peripheral WiFi vision: Exploiting multipath reflections for more sensitive human sensing. WPA 2017 - Proc 4th Int Work Phys Anal co-located with MobiSys 2017. 2017; 13–8.
Wu C, Yang Z, Zhou Z, Liu X, Liu Y, Cao J. Non-invasive detection of moving and stationary human with WiFi. IEEE J Sel Areas Commun. 2015; 33(11): 2329–42.
Zhou R, Lu X, Zhao P, Chen J. Device-Free Presence Detection and Localization with SVM and CSI Fingerprinting. IEEE Sens J. 2017; 17(23): 7990–9.
Zhu H, Xiao F, Sun L, Wang R, Yang P. R-TTWD: Robust Device-Free Through-The-Wall Detection of Moving Human with WiFi. IEEE J Sel Areas Commun. 2017; 35(5): 1090–103.
Nguyen DT, Li W, Ogunbona PO. Human detection from images and videos: A survey. Pattern Recognit [Internet]. 2016; 51: 148–75. Available from: http://dx.doi.org/10.1016/j.patcog.2015.08.027.
Song W, Yu J, Zhao X, Wang A. Research on action recognition and content analysis in videos based on DNN and MLN. Comput Mater Contin. 2019; 61(3): 1189–204.
Copyright (c) 2022 Baghdad Science Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.