Alternative for indoor occupancy estimation based on image processing on embedded system

Authors

DOI:

https://doi.org/10.14482/inde.40.01.620.427

Keywords:

Background subtraction, indoor occupancy, Gaussian Smoothing, Morphological Aperture, Thresholding, Contour Finding, SMTP Protocol, Embedded System, Open Source

Abstract

Indoor occupancy estimation is a process that contributes to maintain quality standards in the areas, and nowadays, it serves as a reference to identify possible foci of infectious respiratory disease contagion. This paper presents a tool for indoor occupancy estimation with Simple Mail Transfer Protocol notification using a Raspberry Pi embedded board. The system is presented as an alternative to conventional occupancy estimation systems by measuring CO2 levels in the area. The method is based on image processing by applying the background subtraction technique using Python programming language. Initially, the area where the system is tested is characterized, and preprocessing, filtering and thresholding stages are applied, as well of notification via e-mail for SMTP. The developed system is compared with a CO2 measurement system by applying a prioritization matrix comparing factors such as detection time, hit rate and implementation costs. The proposed method presented better performance in the totality of the comparison parameters, with a prioritization of 87.972 %. Basing the system in open-source software tools and high level and low-cost hardware tools allows the system to be replicated and implemented on a large scale in controlled environments.

References

A. Franco and F. Leccese, “Measurement of CO2 concentration for occupancy estimation in educational buildings with energy efficiency purposes,” J. Build. Eng., vol. 32, no. August, p. 101714, 2020, doi: 10.1016/j.jobe.2020.101714.

M. Hashemi, “Enlarging smaller images before inputting into convolutional neural network: zero-padding vs. interpolation,” J. Big Data, vol. 6, no. 1, pp. 1–13, 2019, doi: 10.1186/s40537-019-0263-7.

E. N. Kajabad and S. V. Ivanov, “People Detection and Finding Attractive Areas by the use of Movement Detection Analysis and Deep Learning Approach,” Procedia Comput. Sci., vol. 156, pp. 327–337, Jan. 2019, doi: 10.1016/J.PROCS.2019.08.209.

A. Miko?ajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” in 2019 International Interdisciplinary PhD Workshop, IIPhDW 2019, 2019, pp. 117–122.

A. J. Larrazabal, N. Nieto, V. Peterson, D. H. Milone, and E. Ferrante, “Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis,” Proc. Natl. Acad. Sci. U. S. A., vol. 117, no. 23, pp. 12592–12594, 2020, doi: 10.1073/pnas.1919012117.

G. A. Ospina Torres, W. Serna Serna, and G. Daza Santacoloma, “Comparación sistemática de metodologías basadas en información mutua para el registro multimodal de imágenes médicas,” Sci. Tech., vol. 21, no. 4, p. 342, 2016, doi: 10.22517/23447214.12851.

C. H. Setjo, B. Achmad, and Faridah, “Thermal image human detection using Haar-cascade classifier,” Oct. 2017, doi: 10.1109/INAES.2017.8068554.

V. M. Astudillo Delgado and D. A. Revelo Luna, “Apoyo al diagnóstico de neumonía y detección de opacidades pulmonares usando segmentación e instancias semánticas en imágenes de rayos X de tórax,” Rev. Científica Ing. y Desarro., vol. 39, no. 02, pp. 259–274, Jul. 2022, doi: 10.14482/inde.39.2.621.367.

J. L. Ramírez-Arias, A. Rubiano-Fonseca, and R. Jiménez-Moreno, “Object Recognition Through Artificial Intelligence Techniques,” Rev. Fac. Ing., vol. 29, no. 54, p. e10734, 2020, doi: 10.19053/01211129.v29.n54.2020.10734.

F. Fang, K. Qian, B. Zhou, and X. Ma, “Real-time RGB-D based people detection and tracking system for mobile robots,” in 2017 IEEE International Conference on Mechatronics and Automation, ICMA 2017, 2017, pp. 1937–1941, doi: 10.1109/ICMA.2017.8016114.

P. Bazyd?o, K. Lasota, and A. Kozakiewicz, “Botnet Fingerprinting: Anomaly Detection in SMTP Conversations,” IEEE Secur. Priv., vol. 15, no. 6, pp. 25–32, 2017, doi: 10.1109/MSP.2017.4251116.

I. Mutis, A. Ambekar, and V. Joshi, “Real-time space occupancy sensing and human motion analysis using deep learning for indoor air quality control,” Autom. Constr., vol. 116, no. April, p. 103237, 2020, doi: 10.1016/j.autcon.2020.103237.

H. Han, K. Jang, C. Han, and J. Lee, “Occupancy Estimation Based on Co2 Concentration Using Dynamic Neural Network Model,” Aivc.Org, pp. 443–450, 2013.

Y. Yuan, X. Li, Z. Liu, and X. Guan, “Occupancy Estimation in Buildings Based on Infrared Array Sensors Detection,” IEEE Sens. J., vol. 20, no. 2, pp. 1043–1053, Jan. 2020, doi: 10.1109/JSEN.2019.2943157.

A. Szczurek, M. Maciejewska, and T. Pietrucha, “Occupancy determination based on time series of CO2 concentration, temperature and relative humidity,” Energy Build., vol. 147, pp. 142–154, 2017, doi: 10.1016/j.enbuild.2017.04.080.

Y. Zhou et al., “A novel model based on multi-grained cascade forests with wavelet denoising for indoor occupancy estimation,” Build. Environ., vol. 167, no. October 2019, pp. 2–11, 2020, doi: 10.1016/j.buildenv.2019.106461.

S. Zemouri, D. Magoni, A. Zemouri, Y. Gkoufas, K. Katrinis, and J. Murphy, “An Edge Computing Approach to Explore Indoor Environmental Sensor Data for Occupancy Measurement in Office Spaces,” in 2018 IEEE International Smart Cities Conference, ISC2 2018, 2019, pp. 1–9, doi: 10.1109/ISC2.2018.8656753.

D. Giri, S. Shreya, P. Kumari, and R. Yadav, “Indoor human occupancy detection using Machine Learning classification algorithms & their comparison,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1110, no. 1, p. 012020, 2021, doi: 10.1088/1757-899x/1110/1/012020.

E. Longo, A. E. C. Redondi, and M. Cesana, “Accurate occupancy estimation with WiFi and bluetooth/BLE packet capture,” Comput. Networks, vol. 163, no. November, pp. 1–10, 2019, doi: 10.1016/j.comnet.2019.106876.

G. De Cataldo et al., “An upgraded luminosity leveling procedure for the alice experiment,” IEEE Trans. Nucl. Sci., vol. 66, no. 5, pp. 763–770, 2019, doi: 10.1109/TNS.2019.2907227.

C. S. Marzan and N. Marcos, “Towards tobacco leaf detection using Haar cascade classifier and image processing techniques,” ACM Int. Conf. Proceeding Ser., pp. 63–68, 2018, doi: 10.1145/3282286.3282292.

J. Zuo, Z. Jia, J. Yang, and N. Kasabov, “Moving Target Detection Based on Improved Gaussian Mixture Background Subtraction in Video Images,” IEEE Access, vol. 7, pp. 152612–152623, 2019, doi: 10.1109/ACCESS.2019.2946230.

P. Singhal, A. Verma, and A. Garg, “A study in finding effectiveness of Gaussian blur filter over bilateral filter in natural scenes for graph based image segmentation,” 4th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2017, pp. 4–9, Aug. 2017, doi: 10.1109/ICACCS.2017.8014612.

S. Shoba and R. Rajavel, “Image Processing Techniques for Segments Grouping in Monaural Speech Separation,” Circuits, Syst. Signal Process., vol. 37, no. 8, pp. 3651–3670, 2018, doi: 10.1007/s00034-017-0728-x.

C. Shan, B. Huang, and M. Li, “Binary Morphological Filtering of Dominant Scattering Area Residues for SAR Target Recognition,” Comput. Intell. Neurosci., vol. 2018, 2018, doi: 10.1155/2018/9680465.

J. Rudas and G. Sánchez Torres, “Detección de patologías derivadas de las afecciones diabéticas: una revisión del análisis digital de imágenes de retina,” Rev. Científica Ing. y Desarro., pp. 317–338, Dec. 04, 2013.

M. Jin, S. Liu, S. Schiavon, and C. Spanos, “Automated mobile sensing: Towards high-granularity agile indoor environmental quality monitoring,” Build. Environ., vol. 127, no. 1, pp. 268–276, 2018, doi: 10.1016/j.buildenv.2017.11.003.

L. Fleming, D. Gibson, S. Song, C. Li, and S. Reid, “Reducing N2O induced cross-talk in a NDIR CO2 gas sensor for breath analysis using multilayer thin film optical interference coatings,” Surf. Coatings Technol., vol. 336, no. 0, pp. 9–16, 2018, doi: 10.1016/j.surfcoat.2017.09.033.

C. Jiang, M. K. Masood, Y. C. Soh, and H. Li, “Indoor occupancy estimation from carbon dioxide concentration,” Energy Build., vol. 131, pp. 132–141, 2016, doi: 10.1016/j.enbuild.2016.09.002.

G. Ansanay-Alex, “Estimating occupancy using indoor carbon dioxide concentrations only in an office building: a method and qualitative assessment,” in REHVA World Congress on Energy efficient, smart and healthy buildings (CLIMA), 2013, no. April, pp. 1–8.

Published

2022-01-03

How to Cite

[1]
D. A. Castellano Carvajal, C. V. Niño Rondón, B. Medina Delgado, S. A. . Castro Casadiego, and D. . Guevara Ibarra, “Alternative for indoor occupancy estimation based on image processing on embedded system”, Ing. y Des., vol. 40, no. 1, pp. 47–70, Jan. 2022.