Determination of Optimum Threshold for Accuracy of People-counting System Based on Motion Detection

  • Ryu, Hanseul (Intern program, Built Environment Science and Technology Laboratory, Graduate School of Public Health, Seoul National University) ;
  • Song, Junho (Intern program, Built Environment Science and Technology Laboratory, Graduate School of Public Health, Seoul National University) ;
  • Lee, Boram (Department of Environmental Health Graduate School of Public Health, Seoul National University) ;
  • Lee, Kiyoung (Department of Environmental Health Graduate School of Public Health, Seoul National University)
  • Received : 2015.07.06
  • Accepted : 2015.09.25
  • Published : 2015.10.28


Objectives: A people-counting system measures real-time occupancy through motion detection. Accurate people-counting can be used to calculate suitable ventilation demands. This study determined the optimum motion threshold for a people-counting system. Methods: In a closed room with two occupants moving constantly, different thresholds were tested for the accuracy of a people-counting system. The experiments were conducted at 150, 300, 450 and 600 lux. These levels of brightness included the illumination levels of most public indoor areas. The experiments were repeated with three types of clothing coloration. Results: Overall, a threshold of 16 provided the lowest mean error percentage for the people-counting system. Brightness and clothing color did not have a significant impact on the results. Conclusion: A people-counting system could be used with threshold of 16 for most indoor environments.


Grant : 융합기술을 활용한 건강복지 공동이용시설의 통합 시스템의 개발


  1. R. Cantin, A. Kindinis, P. Michel. New approaches for overcoming the complexity of future buildings impacted by new energy constraints. Futures 2012; 44: 735-745.
  2. L. Perez-Lombard, J. Ortiz, C. Pout. A review on buildings energy consumption information. Energy and Buildings 2008; 40: 394-398.
  3. M. Ng, M. Qu, P. Zheng, Z. Li, Y. Hang. $CO_2$-based demand controlled ventilation under new ASHRAE Standard 62.1-2010: a case study for a gymnasium of an elementary school at West Lafayette, Indiana. Energy and Buildings 2011; 43: 11, 3216-3225.
  4. A. Rackes, M. Waring. Modeling impacts of dynamic ventilation strategies on indoor air quality of offices in six US cities. Building and Environment 2013; 60: 243-253.
  5. S. Emmerich, A. Persily. State-Of-The-Art Review of $CO_2$ Demand Controlled Ventilation Technology and Application, NISTIR 6729. National Institute of Standards and technology (2001).
  6. X. Lin, J. Lau. Demand controlled ventilation for multiple zone HVAC systems: $CO_2$-based dynamic reset (RP 1547). HVAC&R Research 2014; 20: 8, 875-888.
  7. T. Lu, X. Lu, M. Viljanen. A novel and dynamic demand-controlled ventilation strategy for $CO_2$ control and energy saving in buildings. Energy and Buildings 2011; 43:9, 2499-2508.
  8. N. Nassif. A robust $CO_2$-based demand-controlled ventilation control strategy for multi-zone HVAC systems. Energy and Buildings 2012; 45: 72-81.
  9. Z. Sun, S. Wang, Z. Ma. In-situ implementation and validation of a $CO_2$-based adaptive demandcontrolled ventilation strategy in a multi-zone office building. Building and Environment 2011; 46: 1, 124-133.
  10. D. Cali, P. Matthes, K. Huchtemann, R. Streblow, D. Muller. $CO_2$ based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings. Building and Environment 2015; 86: 39-49.
  11. Demetriou, D. and H. Khalifa. Evaluation of distributed environmental control systems for improving IAQ and reducing energy consumption in office buildings. Building Simulation 2009; 2:3, 197-214.
  12. T. Labeodan, W. Zeiler, G. Boxem, Y. Zhao. Occupancy measurement in commercial office buildings for demand-driven control applications-A survey and detection system evaluation. Energy and Buildings 2015; 93: 303-314.
  13. M. Mysen, S. Berntsen, P. Nafstad, P. Schild. Occupancy density and benefits of demand-controlled ventilation in Norwegian primary schools. Energy and Buildings 2015; 37: 12, 1234-1240.
  14. E. Naghiyev, M. Gillott, R. Wilson. Three unobtrusive domestic occupancy measurement technologies under qualitative review. Energy and Buildings 2014; 69: 507-514.
  15. S. Wang, J. Burnett, H. Chong. Experimental validation of $CO_2$- based occupancy detection for demand-controlled ventilation. Indoor Built Environment 1999; 8: 6, 377-391.
  16. IESNA 9th Edition Handbook, Illuminating Engineering Society of North America (2010).