An Enhanced Image Segmentation Technique-Based on Motion Detection Algorithm

Authors

  • Zaid Sh. Bakr Al-Nahrain University
  • Hamzah M. Marhoon Al-Nahrain University
  • Ammar Alaythawy Al-Nahrain University

DOI:

https://doi.org/10.37385/jaets.v6i1.6080

Keywords:

IoT, Motion detection, Raspberry Pi 3, Firebase cloud

Abstract

This paper presents a prototype for an intelligent, self-contained theft detection system designed for small-scale applications. Utilizing a Raspberry Pi 3 as the core processing unit, the system employs a motion-detecting camera to monitor a defined area, recording and securely archiving video data on a cloud server upon detecting movement. This cloud-based repository supports real-time analysis, ensuring that data remains available for future reference. Battery-powered configuration enhances the system’s portability, making it adaptable across various environments, such as healthcare for patient monitoring or wildlife tracking for behavioural studies. The design aligns with IoT principles, featuring autonomous operation and cloud connectivity, offering a scalable, flexible solution capable of integration into larger IoT ecosystems for diverse surveillance applications.

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References

Albertengo, G., Debele, F. G., Hassan, W., & Stramandino, D. (2020). On the performance of web services, google cloud messaging and firebase cloud messaging. Digital Communications and Networks, 6(1), 31–37.

Aldahoud, A., Fezari, M., Al-Dahoud, A., & Aqel, D. (2024). A Systematic Review on Applying Machine Learning and Deep Learning on SBCs. WSEAS Transactions on Information Science and Applications, 21, 272–281.

Amiriebrahimabadi, M., Rouhi, Z., & Mansouri, N. (2024). A Comprehensive Survey of Multi-Level Thresholding Segmentation Methods for Image Processing. Archives of Computational Methods in Engineering, 31(6), 3647–3697. https://doi.org/10.1007/s11831-024-10093-8

Amosa, T. I., Sebastian, P., Izhar, L. I., Ibrahim, O., Ayinla, L. S., Bahashwan, A. A., Bala, A., & Samaila, Y. A. (2023). Multi-camera multi-object tracking: A review of current trends and future advances. Neurocomputing, 552, 126558. https://doi.org/https://doi.org/10.1016/j.neucom.2023.126558

Ariza, J. A., & Baez, H. (2022). Understanding the role of single?board computers in engineering and computer science education: A systematic literature review. Computer Applications in Engineering Education, 30(1), 304–329.

Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2024). Explainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions. IEEE Access.

Baruwal Chhetri, M., Tariq, S., Singh, R., Jalalvand, F., Paris, C., & Nepal, S. (2024). Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres. ACM Transactions on Internet Technology, 24(3), 1–22.

Basil, N., & Marhoon, H. M. (2023). Towards evaluation of the PID criteria based UAVs observation and tracking head within resizable selection by COA algorithm. Results in Control and Optimization, 12, 100279.

Basil, N., & Marhoon, H. M. (2024). Correction to: selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: Optimizing trade-offs and critical values using optimization algorithms. E-Prime-Advances in Electrical Engineering, Electronics and Energy, 8, 100589.

Basil, N., Marhoon, H. M., Gokulakrishnan, S., & Buddhi, D. (2022). Jaya optimization algorithm implemented on a new novel design of 6-DOF AUV body: a case study. Multimedia Tools and Applications, 1–26.

Basil, N., Marhoon, H. M., Hayal, M. R., Elsayed, E. E., Nurhidayat, I., & Shah, M. A. (2024). Black-hole optimisation algorithm with FOPID-based automation intelligence photovoltaic system for voltage and power issues. Australian Journal of Electrical and Electronics Engineering, 21(2), 115–127.

Basil, N., Sabbar, B. M., Marhoon, H. M., Mohammed, A. F., & Ma’arif, A. (2024). Systematic Review of Unmanned Aerial Vehicles Control: Challenges, Solutions, and Meta-Heuristic Optimization. International Journal of Robotics and Control Systems, 4(4), 1794–1818.

Chapel, M.-N., & Bouwmans, T. (2020). Moving objects detection with a moving camera: A comprehensive review. Computer Science Review, 38, 100310.

Ding, J., Zhang, Z., Yu, X., Zhao, X., & Yan, Z. (2023). A novel moving object detection algorithm based on robust image feature threshold segmentation with improved optical flow estimation. Applied Sciences, 13(8), 4854.

Garcia-Garcia, B., Bouwmans, T., & Silva, A. J. R. (2020). Background subtraction in real applications: Challenges, current models and future directions. Computer Science Review, 35, 100204.

Garea, S. A., & Das, S. (2024). Image Segmentation Methods: Overview, Challenges, and Future Directions. 2024 Seventh International Women in Data Science Conference at Prince Sultan University (WiDS PSU), 56–61. https://doi.org/10.1109/WiDS-PSU61003.2024.00026

Gowda, V. B., Gopalakrishna, M. T., Megha, J., & Mohankumar, S. (2024). Foreground segmentation network using transposed convolutional neural networks and up sampling for multiscale feature encoding. Neural Networks, 170, 167–175.

Hassan, S., Mujtaba, G., Rajput, A., & Fatima, N. (2024). Multi-object tracking: a systematic literature review. Multimedia Tools and Applications, 83(14), 43439–43492.

Kabir, M. M., Jim, J. R., & Istenes, Z. (2025). Terrain detection and segmentation for autonomous vehicle navigation: A state-of-the-art systematic review. Information Fusion, 113, 102644.

Kang, H. J., Aw, K. L., & Lo, D. (2022). Detecting false alarms from automatic static analysis tools: How far are we? Proceedings of the 44th International Conference on Software Engineering, 698–709.

Kim, M.-J., Kim, S., Lee, B., & Kim, J. (2024). Enhancing Deep Learning-Based Segmentation Accuracy through Intensity Rendering and 3D Point Interpolation Techniques to Mitigate Sensor Variability. Sensors, 24(14), 4475.

Lei, L., Yang, Q., Yang, L., Shen, T., Wang, R., & Fu, C. (2024). Deep learning implementation of image segmentation in agricultural applications: a comprehensive review. Artificial Intelligence Review, 57(6), 149. https://doi.org/10.1007/s10462-024-10775-6

Mahmood, Z. (2024). Digital Image Processing: Advanced Technologies and Applications. In Applied Sciences (Switzerland) (Vol. 14, Issue 14). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/app14146051

Mathe, S. E., Kondaveeti, H. K., Vappangi, S., Vanambathina, S. D., & Kumaravelu, N. K. (2024). A comprehensive review on applications of Raspberry Pi. Computer Science Review, 52, 100636.

Mishra, S., Suman, S. K., & Roy, L. B. (2024). Automated Road Crack Classification Using A Novel Forest Optimization Algorithm For Otsu Thresholding And Hybrid Feature Extraction. International Journal Of Advanced Technology And Engineering Exploration, 11(111), 219–242.

Mohammed, A. F., Basil, N., Abdulmaged, R. B., Marhoon, H. M., Ridha, H. M., Ma’arif, A., & Suwarno, I. (2024). Selection and Evaluation of Robotic Arm based Conveyor Belts (RACBs) Motions: NARMA (L2)-FO (ANFIS) PD-I based Jaya Optimization Algorithm. International Journal of Robotics & Control Systems, 4(1).

Mohammed, A. F., Marhoon, H. M., Basil, N., & Ma’arif, A. (2024). A New Hybrid Intelligent Fractional Order Proportional Double Derivative+ Integral (FOPDD+ I) Controller with ANFIS Simulated on Automatic Voltage Regulator System. International Journal of Robotics & Control Systems, 4(2).

Mukalaf, A. H., Marhoon, H. M., Suwarno, I., & Ma’arif, A. (2023). Design and Manufacturing of Smart Car Security System with IoT-Based Real-Time Tracking. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 745–752.

Mustafa, F. E., Ahmed, I., Basit, A., Malik, S. H., Mahmood, A., & Ali, P. R. (2023). A review on effective alarm management systems for industrial process control: barriers and opportunities. International Journal of Critical Infrastructure Protection, 41, 100599.

Pang, S., Thio, T. H. G., Siaw, F. L., Chen, M., & Xia, Y. (2024). Research on Improved Image Segmentation Algorithm Based on GrabCut. Electronics (Switzerland), 13(20). https://doi.org/10.3390/electronics13204068

Rayed, Md. E., Islam, S. M. S., Niha, S. I., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2024). Deep learning for medical image segmentation: State-of-the-art advancements and challenges. Informatics in Medicine Unlocked, 47, 101504. https://doi.org/https://doi.org/10.1016/j.imu.2024.101504

Safaldin, M., Zaghden, N., & Mejdoub, M. (2024). An Improved YOLOv8 to Detect Moving Objects. IEEE Access, 12, 59782–59806. https://doi.org/10.1109/ACCESS.2024.3393835

Singh, K. U., Varshney, N., Gupta, P., Kumar, G., Singh, T., & Dogiwal, S. R. (2024). Mobile Application Control with Firebase Cloud Messaging. International Conference On Innovative Computing And Communication, 527–535.

Thakur, A., & Mishra, S. K. (2024). An in-depth evaluation of deep learning-enabled adaptive approaches for detecting obstacles using sensor-fused data in autonomous vehicles. Engineering Applications of Artificial Intelligence, 133, 108550.

Wahab, F., Ullah, I., Shah, A., Khan, R. A., Choi, A., & Anwar, M. S. (2022). Design and implementation of real-time object detection system based on single-shoot detector and OpenCV. Frontiers in Psychology, 13, 1039645.

Wan, S., Ding, S., & Chen, C. (2022). Edge computing enabled video segmentation for real-time traffic monitoring in internet of vehicles. Pattern Recognition, 121, 108146.

Wang, B., Liu, J., Zhu, S., Xu, F., & Liu, C. (2023). A dual-input moving object detection method in remote sensing image sequences via temporal semantics. Remote Sensing, 15(9), 2230.

Xu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., & Liu, F. (2024). Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches. Bioengineering, 11(10), 1034.

Yazdi, M., & Bouwmans, T. (2018). New trends on moving object detection in video images captured by a moving camera: A survey. Computer Science Review, 28, 157–177.

You, Y., Cao, J., & Zhou, W. (2020). A survey of change detection methods based on remote sensing images for multi-source and multi-objective scenarios. Remote Sensing, 12(15), 2460.

Zamalieva, D., & Yilmaz, A. (2014). Background subtraction for the moving camera: A geometric approach. Computer Vision and Image Understanding, 127, 73–85.

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Published

2024-12-15

How to Cite

Bakr, Z. S., M. Marhoon, H., & Alaythawy, A. (2024). An Enhanced Image Segmentation Technique-Based on Motion Detection Algorithm. Journal of Applied Engineering and Technological Science (JAETS), 6(1), 69–85. https://doi.org/10.37385/jaets.v6i1.6080