Authors : NDIZEYE Maurice; Dr. Emmanuel BUGINGO

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/mrcv2m9u

Scribd : https://tinyurl.com/2muws9k5

DOI : https://doi.org/10.5281/zenodo.14959415

Abstract : This study addresses the critical need for advanced threat detection systems by enhancing real-time video surveillance with automated anomaly detection and response. It integrates machine learning algorithms for object and activity recognition, leveraging AI and parallel computing for efficient and accurate anomaly detection. The research evaluates the adaptability and effectiveness of machine learning models in diverse environments, emphasizing robust response protocols for identified anomalies. Key findings highlight challenges in identifying suspects amidst distractions like vehicles, animals, and crowds. Advanced preprocessing and machine learning techniques significantly improved detection accuracy from 70.4% to 93%. Parallel computing reduced model training time from 71 minutes to just 8 minutes, showcasing its efficiency. The study recommends cost-effective solutions like building high-performance computing (HPC) clusters using Raspberry Pi nodes for training and employing Hadoop clusters for video stream processing. Deploying optimized models on scalable web servers enables real-time diagnostics and anomaly alerts, empowering security operators with faster and more actionable insights. These contributions advance the development of intelligent, efficient, and scalable surveillance systems that address modern security challenges. The proposed methods improve decision-making and real-time responsiveness, providing a significant leap forward in video surveillance technology.

References :

1. Ahmed Elmetwally, R. E. (2024). Deep learning based anomaly detection in real-time video. Deep learning based anomaly detection in real-time video, 1705-1716.
2. Ali, M. M. (2022). Real-time video anomaly detection for smart surveillance. Real-time video anomaly detection for smart surveillance, 1375-1388.
3. Ali, M. M. (2022). Real-time video anomaly detection for smart surveillance. Real-time video anomaly detection for smart surveillance, 1375-1388.
4. Ankush Balaram Pawar1Gayatri Shitole2, S. N. (2024). Intelligence Video Surveillance Using Deep Learning . Software Computing and Testing, 2456-2351.
5. Bansod, S. (2019). Transfer Learning for video anomaly detection. Journal of Intelligent & Fuzzy Systems, 1-9.
6. Basha, S. H. (2022). An information-rich sampling technique over spatio-temporal CNN for classification of human actions in videos. Multimedia tools and applications, 40431-40449.
7. Brown, S. (2021, Apr 21). MIT. Retrieved from mitsloan.mit.edu: https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained
8. Brownlee, J. (2019, September 16). A Gentle Introduction to Transfer Learning for Deep Learning. Retrieved from https://machinelearningmastery.com/: https://machinelearningmastery.com/transfer-learning-for-deep-learning/
9. Brownlee, J. (2023). Logistic Regression for Machine Learning. Machine Learning Mastery, 1-7.
10. Brownlee, J. (2023, November 16). Machine Learning Mastery. Retrieved from machinelearningmastery.com: https://machinelearningmastery.com/what-is-deep-learning/
11. Dalibor Klusacek, G. P. (2023, May 19). Job Scheduling Strategies for Parallel Processing. Retrieved from Job Scheduling Strategies for Parallel Processing: https://www.google.rw/books/edition/Job_Scheduling_Strategies_for_Parallel_P/M1DXEAAAQBAJ?hl=en&gbpv=1
12. Dobbin, J. (2019, February 24). GPU vs CPU: What Matters Most for PC Gaming? Retrieved from https://www.hp.com/: https://www.hp.com/us-en/shop/tech-takes/gpu-vs-cpu-for-pc-gaming
13. Eremenko, K. (2018). Deep Learning A-Z™: Convolutional Neural Networks (CNN) - Step 3: Flattening. Flattening, 3-5.
14. Ethier, S. (2022, October 19). Parallel Computing: Intro to MPI. Retrieved from https://researchcomputing.princeton.edu/: https://researchcomputing.princeton.edu/sites/g/files/toruqf311/files/documents/MPI_tutorial_Fall_Break_2022.pdf
15. Ethier, S. (2022, October 19). Parallel Computing: Intro to MPI. Retrieved from https://researchcomputing.princeton.edu/: https://researchcomputing.princeton.edu/sites/g/files/toruqf311/files/documents/MPI_tutorial_Fall_Break_2022.pdf
16. Gandhi, R. (2018, May 27). Introduction to Machine Learning Algorithms: Linear Regression. Retrieved from towardsdatascience.com: https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a
17. geeksforgeeks. (2024, Sep 04). https://www.geeksforgeeks.org. Retrieved from https://www.geeksforgeeks.org: https://www.geeksforgeeks.org/cnn-introduction-to-pooling-layer/
18. geeksforgeeks.org. (2024, Sep 04). https://www.geeksforgeeks.org/. Retrieved from geeksforgeeks.org: https://www.geeksforgeeks.org/cnn-introduction-to-pooling-layer/
19. Gobbert, M. K. (2021, April 3). High Performance Computing Facility. Retrieved from hpcf.umbc.edu: https://hpcf.umbc.edu/ada/slurm/overview/
20. Haohan Ding, J. T. (2023). The Application of Artificial Intelligence and Big Data in the Food Industry. MDPI, 29.
21. Heller, M. (2022, Sep 16). infoworld. Retrieved from infoworld.com: https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html
22. ibm.com. (2023, Dec 19). www.ibm.com. Retrieved from www.ibm.com: https://www.ibm.com/think/topics/machine-learning-for-anomaly-detection
23. Kim, J. S. (2021). A study on implementation of real-time intelligent video surveillance system based on embedded module. EURASIP Journal on Image and Video Processing, 2-22.
24. Kim, Y. M.-O. (2023). A Survey of Video Surveillance Systems in Smart City. MDPI, 3-7.
25. Kraus, J. (2013, March 13). An Introduction to CUDA-Aware MPI. Retrieved from https://developer.nvidia.com/: https://developer.nvidia.com/blog/introduction-cuda-aware-mpi/
26. Mishra, M. (2020, Aug 26). Convolutional Neural Networks, Explained. Retrieved from towardsdatascience.com: https://towardsdatascience.com/convolutional-neural-networks-explained-9cc5188c4939
27. Muts, I. (2023). Real-Time Video Monitoring: The New Era of Remote Video Surveillance. Euristiq, 3-14.
28. Nielsen, F. (2016). Introduction to HPC with MPI for Data Science. Introduction to MPI: The Message Passing Interface, 21-62.
29. Nishihara, R. (2019, Feb 11). Modern Parallel and Distributed Python: A Quick Tutorial on Ray. Retrieved from https://towardsdatascience.com/: https://towardsdatascience.com/modern-parallel-and-distributed-python-a-quick-tutorial-on-ray-99f8d70369b8
30. NVIDIA Tesla P100 PCIe 16 GB. (n.d.). Retrieved from techpowerup: https://www.techpowerup.com/gpu-specs/tesla-p100-pcie-16-gb.c2888
31. Rosebrock, A. (2021, May 14). Convolutional Neural Networks (CNNs) and Layer Types. Retrieved from pyimagesearch.com: https://pyimagesearch.com/2021/05/14/convolutional-neural-networks-cnns-and-layer-types/
32. Rosebrock, A. (2021, May 14). pyimagesearch. Retrieved from pyimagesearch.com: https://pyimagesearch.com/2021/05/14/convolutional-neural-networks-cnns-and-layer-types/
33. Saha, S. (2018, Dec 15). A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way. Retrieved from towardsdatascience.com: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
34. Santilli, P. (2024, January 11). Real-time Threat Detection: The Power of AI in Video Surveillance. Retrieved from scip.org: https://www.scip.org/news/662235/Real-time-Threat-Detection-The-Power-of-AI-in-Video-Surveillance.htm
35. Singh, S. (2023, July 20). What is a Conceptual Framework and How to Make It (with Examples). Retrieved from https://researcher.life/: https://researcher.life/blog/article/what-is-a-conceptual-framework-and-how-to-make-it-with-examples/
36. Stratified Random Sampling. (n.d.). Retrieved from Questionpro: https://www.questionpro.com/blog/stratified-random-sampling/
37. Strickland, J. (2024, march 6). Parallel Computing. Retrieved from https://computer.howstuffworks.com/: https://computer.howstuffworks.com/parallel-processing.htm
38. Techlab, N. (2024, June 12). www.netlabindia.com. Retrieved from www.netlabindia.com: https://www.netlabindia.com/blogs/emerging-technologies-shaping-the-future-of-video-surveillance/
39. Unzueta, D. (2022, October 18). Fully Connected Layer vs. Convolutional Layer: Explained. Retrieved from https://builtin.com/: https://builtin.com/machine-learning/fully-connected-layer
40. Venujkvenk. (2023, Oct 03). Anomaly Detection Techniques: A Comprehensive Guide with Supervised and Unsupervised Learning. Retrieved from www.medium.com: https://medium.com/@venujkvenk/anomaly-detection-techniques-a-comprehensive-guide-with-supervised-and-unsupervised-learning-67671cdc9680
41. Xidong Wu, P. B. (2023). Performance and Energy Consumption of Parallel Machine Learning Algorithms. ECE 2166.
42. Yasar, K. (2023, March 31). https://www.techtarget.com/searchenterpriseai/definition/image-recognition. Retrieved from www.techtarget.com: https://www.techtarget.com/searchenterpriseai/definition/image-recognition
43. Yim, J. (2015). Image Classification Using Convolutional Neural Networks With Multi-stage Feature. Robot Intelligence Technology and Applications 3, 587-594.
44. Yokim, S. (2018, Aug 20). Tensor Ops Made Easier in cuDNN. Retrieved from developer.nvidia.com: https://developer.nvidia.com/blog/tensor-ops-made-easier-in-cudnn/

45. Zamora, R. R. (2021). Parallel and High Performance Computing. Parallel Programming (MPI) and Batch Usage (SLURM), 650-704