{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T04:59:40Z","timestamp":1769921980601,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001858","name":"VINNOVA","doi-asserted-by":"publisher","award":["2017-02798"],"award-info":[{"award-number":["2017-02798"]}],"id":[{"id":"10.13039\/501100001858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Knowledge about the indoor occupancy is one of the important sources of information to design smart buildings. In some applications, the number of occupants in each zone is required. However, there are many challenges such as user privacy, communication limit, and sensor\u2019s computational capability in development of the occupancy monitoring systems. In this work, a people flow counting algorithm has been developed which uses low-resolution thermal images to avoid any privacy concern. Moreover, the proposed scheme is designed to be applicable for wireless sensor networks based on the internet-of-things platform. Simple low-complexity image processing techniques are considered to detect possible objects in sensor\u2019s field of view. To tackle the noisy detection measurements, a multi-Bernoulli target tracking approach is used to track and finally to count the number of people passing the area of interest in different directions. Based on the sensor node\u2019s processing capability, one can consider either a centralized or a full in situ people flow counting system. By performing the tracking part either in sensor node or in a fusion center, there would be a trade off between the computational complexity and the transmission rate. Therefore, the developed system can be performed in a wide range of applications with different processing and transmission constraints. The accuracy and robustness of the proposed method are also evaluated with real measurements from different conducted trials and open-source dataset.<\/jats:p>","DOI":"10.3390\/rs13163127","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:35:40Z","timestamp":1628458540000},"page":"3127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Multi-Bernoulli Tracking Approach for Occupancy Monitoring of Smart Buildings Using Low-Resolution Infrared Sensor Array"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7730-7778","authenticated-orcid":false,"given":"Ramtin","family":"Rabiee","sequence":"first","affiliation":[{"name":"Department of Applied Physics and Electronics, Ume\u00e5 University, 901 87 Ume\u00e5, Sweden"}]},{"given":"Johannes","family":"Karlsson","sequence":"additional","affiliation":[{"name":"Department of Applied Physics and Electronics, Ume\u00e5 University, 901 87 Ume\u00e5, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Beltran, A., Erickson, V.L., and Cerpa, A.E. (2013, January 14\u201315). ThermoSense: Occupancy Thermal Based Sensing for HVAC Control. Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, Rome, Italy.","DOI":"10.1145\/2528282.2528301"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9785","DOI":"10.1109\/TIE.2018.2818665","article-title":"Smart Sensing for HVAC Control: Collaborative Intelligence in Optical and IR Cameras","volume":"65","author":"Cao","year":"2018","journal-title":"IEEE Trans. Ind. Electr."},{"key":"ref_3","unstructured":"Yang, Z., Li, N., Becerik-Gerber, B., and Orosz, M. (2012, January 26\u201330). A Multi-sensor Based Occupancy Estimation Model for Supporting Demand Driven HVAC Operations. Proceedings of the 2012 Symposium on Simulation for Architecture and Urban Design, Orlando, FL, USA."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.enbuild.2018.08.039","article-title":"Propagating sensor uncertainty to better infer office occupancy in smart building control","volume":"179","author":"Papatsimpa","year":"2018","journal-title":"Energy Build."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.procs.2018.07.151","article-title":"Towards a Real-time Occupancy Detection Approach for Smart Buildings","volume":"134","author":"Elkhoukhi","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Akkaya, K., Guvenc, I., Aygun, R., Pala, N., and Kadri, A. (2015, January 9\u201312). IoT-based occupancy monitoring techniques for energy-efficient smart buildings. Proceedings of the 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), New Orleans, LA, USA.","DOI":"10.1109\/WCNCW.2015.7122529"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ekwevugbe, T., Brown, N., and Fan, D. (2012, January 18\u201320). A design model for building occupancy detection using sensor fusion. Proceedings of the 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST), Campione d\u2032Italia, Italy.","DOI":"10.1109\/DEST.2012.6227924"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sangoboye, F.C., and Kj\u00e6rgaard, M.B. (2016, January 16\u201317). PLCount: A Probabilistic Fusion Algorithm for Accurately Estimating Occupancy from 3D Camera Counts. Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, Palo Alto, CA, USA.","DOI":"10.1145\/2993422.2993575"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Berger, M., and Armitage, A. (2010, January 23\u201324). Room occupancy measurement using low-resolution infrared cameras. Proceedings of the IET Irish Signals and Systems Conference (ISSC 2010), Cork, Ireland.","DOI":"10.1049\/cp.2010.0521"},{"key":"ref_10","unstructured":"Basu, C., and Rowe, A. (2015). Tracking Motion and Proxemics using Thermal-sensor Array. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1109\/JSEN.2019.2943157","article-title":"Occupancy Estimation in Buildings Based on Infrared Array Sensors Detection","volume":"20","author":"Yuan","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Mohammadmoradi, H., Munir, S., Gnawali, O., and Shelton, C. (2017, January 5\u20137). Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors. Proceedings of the 2017 13th International Conference on Distributed Computing in Sensor Systems (DCOSS), Ottawa, ON, Canada.","DOI":"10.1109\/DCOSS.2017.26"},{"key":"ref_13","unstructured":"Panasonic Industry (2021, August 06). Infrared Array Sensor\u2014Grid-EYE. Available online: https:\/\/eu.industrial.panasonic.com\/products\/sensors-optical-devices\/sensors-automotive-and-industrial-applications\/infrared-array."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1214\/10-STS325","article-title":"Particle Learning and Smoothing","volume":"25","author":"Carvalho","year":"2010","journal-title":"Stat. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Urteaga, I., Bugallo, M.F., and Djuri\u0107, P.M. (2016, January 26\u201329). Sequential Monte Carlo methods under model uncertainty. Proceedings of the 2016 IEEE Statistical Signal Processing Workshop (SSP), Palma de Mallorca, Spain.","DOI":"10.1109\/SSP.2016.7551747"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.dsp.2016.09.011","article-title":"Cooperative parallel particle filters for online model selection and applications to urban mobility","volume":"60","author":"Martino","year":"2017","journal-title":"Digit. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Sun, J., Zhou, H., and Xu, C. (2021). Group Target Tracking Based on MS-MeMBer Filters. Remote Sens., 13.","DOI":"10.3390\/rs13101920"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TSP.2013.2259822","article-title":"Labeled Random Finite Sets and Multi-Object Conjugate Priors","volume":"61","author":"Vo","year":"2013","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6554","DOI":"10.1109\/TSP.2014.2364014","article-title":"Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter","volume":"62","author":"Vo","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1007\/s00138-018-0963-6","article-title":"Visual tracking of resident space objects via an RFS-based multi-Bernoulli track-before-detect method","volume":"29","author":"Javanmardi","year":"2018","journal-title":"Mach. Vis. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, C., and Wang, W. (2018). Detection and Tracking of Moving Targets for Thermal Infrared Video Sequences. Sensors, 18.","DOI":"10.3390\/s18113944"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1109\/TSP.2014.2323064","article-title":"The Labeled Multi-Bernoulli Filter","volume":"62","author":"Reuter","year":"2014","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1109\/TSP.2016.2641392","article-title":"An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter","volume":"65","author":"Vo","year":"2017","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1007\/s11554-020-00943-6","article-title":"Communication and computation inter-effects in people counting using intelligence partitioning","volume":"17","author":"Shallari","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mahler, R.P.S. (2007). Statistical Multisource-Multitarget Information Fusion, Artech House, Inc.","DOI":"10.1201\/9781420053098.ch16"},{"key":"ref_26","unstructured":"Elektroniksystem i Ume\u00e5 AB (ELSYS) (2020, December 10). Elsys\u2014ERS Eye. Available online: https:\/\/www.elsys.se\/en\/ers-eye\/."},{"key":"ref_27","unstructured":"Panasonic Automotive & Industrial Systems Europe (2020, December 10). Grid-EYE Characteristics (2020-10-15). Available online: https:\/\/industry.panasonic.eu\/components\/sensors\/grid-eye."},{"key":"ref_28","unstructured":"Freitas, R.A. (1999). Nanomedicine, Volume 1: Basic Capabilities, Landes Bioscience."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/BF02584515","article-title":"Effect of ambient temperature on the thermal profile of the human forearm, hand, and fingers","volume":"4","author":"Montgomery","year":"1976","journal-title":"Ann. Biomed. Eng."},{"key":"ref_30","unstructured":"Kawanishi, Y. (2020, December 16). Nagoya University Extremely Low-Resolution FIR Image Action Dataset (Ver. 2018). Available online: https:\/\/www.murase.m.is.nagoya-u.ac.jp\/~kawanishiy\/en\/datasets.html."},{"key":"ref_31","unstructured":"Kawashima, T., Kawanishi, Y., Ide, I., Murase, H., Deguchi, D., Aizawa, T., and Kawade, M. (September, January 29). Action recognition from extremely low-resolution thermal image sequence. Proceedings of the 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, Lecce, Italy."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:42:05Z","timestamp":1760164925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,7]]},"references-count":31,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163127"],"URL":"https:\/\/doi.org\/10.3390\/rs13163127","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,7]]}}}