Design and Optimization of AI-Driven Image Processing Pipelines for Smart Cameras

Design and Optimization of AI-Driven Image Processing Pipelines for Smart Cameras
  IJETT-book-cover           
  
© 2025 by IJETT Journal
Volume-73 Issue-5
Year of Publication : 2025
Author : Brayan Vizarreta Huayapa, Rodolfo Churata Quispe, Jesus Talavera S
DOI : 10.14445/22315381/IJETT-V73I5P104

How to Cite?
Brayan Vizarreta Huayapa, Rodolfo Churata Quispe, Jesus Talavera S, "Design and Optimization of AI-Driven Image Processing Pipelines for Smart Cameras," International Journal of Engineering Trends and Technology, vol. 73, no. 5, pp.30-37, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I5P104

Abstract
This paper presents a new design and optimization framework for the AI-driven image-processing pipeline dedicated to smart cameras that overcome main computational efficiency, energy consumption, and flexibility challenges. The proposed pipeline integrates state-of-the-art deep learning models, such as YOLOv5 and Faster R-CNN, along with optimization techniques, including model quantization and pruning. The modular architecture is flexible for the integration of new algorithms and technologies. Extensive validation was made in urban traffic surveillance and residential security contexts in Arequipa, Peru, which shows significant improvements in accuracy, processing speed, and energy efficiency. The deployed pipeline achieves, on average, 92% precision, 89% recall rate, and 90.5% F1 score on the urban traffic monitoring domain while improving the processing speed by 40% and reducing energy consumption by 35%. For intrusion detection in home security, it detects with 88% accuracy, FPR of 5%, and FNR of 7%. Due to the modular nature of this design, it reduces the integration time of new functionalities by 60%. These results give reason for the robustness and feasibility of the pipeline that can be deployed in resource-constrained environments, opening perspectives toward wider diffusion and further research on AI-driven smart cameras.

Keywords
AI-driven image processing, Smart cameras, Deep learning models, Urban traffic surveillance, Residential security.

References
[1] Apoorv Agha, Rishabh Ranjan, and Woon-Seng Gan, “Noisy Vehicle Surveillance Camera: A System to Deter Noisy Vehicle in Smart City,” Applied Acoustics, vol. 117, pp. 236-245, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Tan Zhang et al., “The Design and Implementation of a Wireless Video Surveillance System,” Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 426-438, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Dat Tien Nguyen et al., “Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras,” Sensors, vol. 17, no. 3, pp. 1-29, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Arequipa Loses More than 700 Million Soles a Year due to Traffic Congestion, Automotive Association of Peru, 2023. [Online]. Available: https://aap.org.pe/arequipa-pierde-mas-de-700-millones-de-soles-al-ano-por-congestion-vehicular-agencia-nacional-de-transito-y-seguridad-vial-plan-urbano-de-movilidad/
[5] Elias Kougianos et al., “Design of a High-Performance System for Secure Image Communication in the Internet of Things,” IEEE Access, vol. 4, pp. 1222-1242, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Tomasz Kryjak, Mateusz Komorkiewicz, and Marek Gorgon, “Real-Time Hardware-Software Embedded Vision System for its Smart Camera Implemented in Zynq Soc,” Journal of Real-Time Image Processing, vol. 15, pp. 123-159, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Shaoqing Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” arXiv, pp. 1-14, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
[Google Scholar] [Publisher Link]
[9] Tsung-Yi Lin et al., “Focal Loss for Dense Object Detection,” Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988, 2017.
[Google Scholar] [Publisher Link]
[10] Ge Yan et al., “Camfi: An AI-Driven and Camera-Based System for Assisting Users in Finding Lost Objects in Multi-Person Scenarios,” CHI Conference on Human Factors in Computing Systems Extended Abstracts, New Orleans LA, USA, pp. 1-7, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Harish Reddy Gantla et al., “Fusion of Real-Time Traffic and Environmental Sensor Data with Machine Learning for Optimizing Smart City Operations,” Fusion: Practice and Applications, vol. 19, no. 2, pp. 328-340, 2025.
[CrossRef] [Publisher Link]
[12] Clément Farabet et al., “Hardware Accelerated Convolutional Neural Networks for Synthetic Vision Systems,” Proceedings of 2010 IEEE International Symposium on Circuits and Systems, Paris, France, pp. 257-260, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Juan Guerrero-Ibáñez, Sherali Zeadally, and Sherali Zeadally, “Sensor Technologies for Intelligent Transportation Systems,” Sensors, vol. 18, no. 4, pp. 1-24, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Wenzhi Wu, and Ying Wei, “Research on Control of Computer Room Personnel Based on OpenCV,” International Symposium on Computer Technology and Information Science, Guilin, China, pp. 6-9, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Irfan Kilic, and Galip Aydin, “Traffic Sign Detection and Recognition Using Tensorflow’s Object Detection API with a New Benchmark Dataset,” International Conference on Electrical Engineering, Istanbul, Turkey, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Numan Senel et al., “Multi-Sensor Data Fusion for Real-Time Multi-Object Tracking,” Processes, vol. 11, no. 2, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Thomas W. Sanchez, Marc Brenman, and Xinyue Ye, “The Ethical Concerns of Artificial Intelligence in Urban Planning,” Journal of the American Planning Association, vol. 91, no. 2, pp. 294-307, 2025.
[CrossRef] [Google Scholar] [Publisher Link]