Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.1501656 Computer Software Software Architecture Software Engineering (Other) Bilgisayar Yazılımı Yazılım Mimarisi Yazılım Mühendisliği (Diğer) Embedded System-Based Image Processing Methods for Detection of Forensic Events in CCTV Videos https://orcid.org/0000-0001-5079-2825 Kılıç İrfan FIRAT ÜNİVERSİTESİ https://orcid.org/0009-0000-2214-2518 Orhan Emre FIRAT UNIVERSITY https://orcid.org/0009-0001-0556-7757 Özel Muhammed Enes FIRAT UNIVERSITY https://orcid.org/0009-0005-8608-400X Arslan Gözde FIRAT UNIVERSITY https://orcid.org/0000-0001-9623-2284 Yaman Orhan FIRAT UNIVERSITY 01 07 2025 12 4 330 336 06 15 2024 12 30 2024 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

Nowadays, there is no place where security cameras (CCTV) are not used. Security cameras play a huge role in solving criminal cases. However, a lot of time is spent examining these camera recordings. This situation causes the incidents to not be resolved and causes delays. This study, it is aimed to use machine learning to increase the size of security camera recordings with efficient algorithms that can work on devices with low processing power such as embedded systems. Within the scope of the study, an experimental environment was created by installing a security camera system. Fast and effective video reduction algorithms have been developed on videos collected in different scenarios. New approaches called hopscotch and lens algorithms have been presented for video reduction. These approaches are aimed to obtain rapid results by applying them to security camera videos. It is thought that the developed video reduction approaches will lead to the creation of applicable prototypes on embedded cards such as Raspberry Pi. PSNR (Peak Signal to Noise Ratio) metric was used to compare the images. Real-time results were obtained with our approaches applied to images.

 Embedded system Forensic investigation Video reduction Image processing CCTV PSNR TUBİTAK 1919B012218080 [1] Paul Bischoff, “Surveillance camera statistics: which are the most surveilled cities?,” 2023. Available: https://www.comparitech.com/vpn-privacy/the-worlds-most-surveilled-cities/. [Accessed: Mar. 30, 2024] [2] Y. C. Huang, Y. H. Chen, C. Y. Lu, H. P. Wang, W. H. Peng, and C. C. Huang, “Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2021. doi: 10.1109/CVPR46437.2021.00353 [3] Z. Li, B. Geng, X. Tao, Y. Duan, T. Li, and J. Huang, “EEG-Based VR Video Quality Measurement for Resolution Reduction,” in 2023 IEEE International Conference on Communications Workshops: Sustainable Communications for Renaissance, ICC Workshops 2023, 2023. doi: 10.1109/ICCWorkshops57953.2023.10283518 [4] D. Hazra and Y. C. Byun, “Upsampling real-time, low-resolution CCTV videos using generative adversarial networks,” Electron., 2020, doi: 10.3390/electronics9081312 [5] M. Adnan Arefeen, S. Tabassum Nimi, and M. Yusuf Sarwar Uddin, “FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics,” in Proceedings - 18th Annual International Conference on Distributed Computing in Sensor Systems, DCOSS 2022, 2022. doi: 10.1109/DCOSS54816.2022.00033 [6] T. Wang, Z. Meng, M. Xu, R. Han, and H. Liu, “Enabling high frame-rate UHD real-time communication with frame-skipping,” in HotEdgeVideo 2021 - Proceedings of the 2021 3rd ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges, 2021. doi: 10.1145/3477083.3481582 [7] J. P. C. Cempron and J. P. Ilao, “Network Video Frame-Skip Modeling and Simulation,” in 2021 27th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2021, 2021. doi: 10.1109/M2VIP49856.2021.9664996 [8] M. Ajmal, M. H. Ashraf, M. Shakir, Y. Abbas, and F. A. Shah, “Video summarization: Techniques and classification,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7594 LNCS, pp. 1–13, 2012, doi: 10.1007/978-3-642-33564-8_1 [9] “Rasperry Pi,” 2023. Available: https://www.raspberrypi.org/. [Accessed: Dec. 15, 2023] [10] “Odroid,” 2023. Available: https://www.hardkernel.com/. [Accessed: Dec. 15, 2023] [11] “UP2 Board,” 2023. Available: https://up-board.org/about-up/. [Accessed: Dec. 15, 2023] [12] H. C. Kang, H. N. Han, H. C. Bae, M. G. Kim, J. Y. Son, and Y. K. Kim, “Hsv color‐space‐based automated object localization for robot grasping without prior knowledge,” Appl. Sci., 2021, doi: 10.3390/app11167593 [13] “PSNR Mathworks,” 2023. Available: https://www.mathworks.com/help/images/ref/psnr.html. [Accessed: Dec. 20, 2023] [14] J. Korhonen and J. You, “Peak signal-to-noise ratio revisited: Is simple beautiful?,” in 2012 4th International Workshop on Quality of Multimedia Experience, QoMEX 2012, 2012. doi: 10.1109/QoMEX.2012.6263880 [15] Al Boivik, HANDBOOK OF IMAGE AND VIDEO PROCESSING, First Edit. Canada: Academic Press, 2000.