Abstract
This paper evaluates an AI video surveillance application on diverse high-performance computing (HPC) architectures. AI-powered video surveillance has emerged as a vital tool for security and monitoring, relying on hardware infrastructure for efficient processing. We present a benchmark of an AI application based on the YOLO object dection framework to track downed pepole in critical scenarios. This study investigates the impact of different architectural designs, including CPUs and GPUs on video analysis performance. Evaluation metrics encompass computational speed, power consumption and resource utilisation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sharma V et al (2021) Video processing using deep learning techniques: a systematic literature review. IEEE Access 9:139489–139507. https://doi.org/10.1109/ACCESS.2021.3118541
Wang X (2013) Intelligent multi-camera video surveillance: a review. Pattern Recognit Lett 34(1). Extracting Semantics from Multi-Spectrum Video, pp. 3–19. issn: 0167–8655. https://doi.org/10.1016/j.patrec.2012.07.005, https://www.sciencedirect.com/science/article/pii/S016786551200219X
Dilshad N et al (2020) Applications and challenges in video surveillance via drone: a brief survey. In: 2020 international conference on information and communication technology convergence (ICTC), pp 728–732. https://doi.org/10.1109/ICTC49870.2020.9289536
Webb JA (1994) High performance computing in image processing and computer vision. In: Proceedings of the 12th IAPR international conference on pattern recognition, vol. 2—conference B: computer vision and image processing (Cat. No.94CH3440-5), vol 3, pp 218–222. ICPR.1994.577165. https://doi.org/10.1109/ICPR.1994.577165
Cococcioni M et al (2020) Fast deep neural networks for image processing using posits and ARM scalable vector extension. J R-Time Image Process 17(3):759–771. issn: 1861-8219. https://doi.org/10.1007/s11554020-00984-x
Cococcioni M et al (2021) Faster deep neural network image processing by using vectorized posit operations on a RISC-V processor. In: Kehtarnavaz N, Carlsohn MF (eds) Real-time image processing and deep learning, vol 11736. International Society for Optics and Photonics. SPIE, p 1173604. https://doi.org/10.1117/12.2586565
Yi S et al (2012) The model of face recognition in video surveillance based on cloud computing. In: Jin D, Lin S (eds) Advances in computer science and in-formation engineering. Springer, Berlin, pp 105–111. isbn: 978-3-642-30126-1
Jocher G et al, ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation. Version v7.0. Nov. 2022.5281/zenodo.7347926. https://doi.org/10.5281/zenodo.7347926
Wojke N, Bewley A (2018) Deep cosine metric learning for person re-identification. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 748–756. https://doi.org/10.1109/WACV.2018.00087
Bai J, Lu F, Zhang K et al (2019) ONNX: open neural network ex-change. github.com/onnx/onnx
Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE international conference on image processing (ICIP). IEEE, pp 3645–3649. https://doi.org/10.1109/ICIP.2017.8296962
Ploco A, Rodriguez AM, Geradts Z (2020) Spatial-temporal omni-scale feature learning for person re-identification. In: 2020 8th international workshop on biometrics and forensics (IWBF), pp 1–5. https://doi.org/10.1109/IWBF49977.2020.9107966
Acknowledgments
EuPilot: the European PILOT project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No.101034126. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Spain, Italy, Switzerland, Germany, France, Greece, Sweden, Croatia and Turkey. Italian Ministry of Education and Research (MUR), ForeLab project (Departments of Excellence), and by PNRR—M4C2—Investimento 1.3, Partenariato Esteso PE00000013—“FAIR—Future Artificial Intelligence Research"—Spoke 1 “Human-centered AI” (the PNRR program is funded by the European Commission under the NextGeneration EU programme).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rossi, F., Mugnaini, G., Saponara, S., Cavazzoni, C., Sciarappa, A. (2024). Evaluation of AI and Video Computing Applications on Multiple Heterogeneous Architectures. In: Bellotti, F., et al. Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2023. Lecture Notes in Electrical Engineering, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-031-48121-5_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-48121-5_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48120-8
Online ISBN: 978-3-031-48121-5
eBook Packages: EngineeringEngineering (R0)