Crowd detection and occupancy estimation through indirect environmental measurements

Viani, Federico; Polo, Alessandro; Robol, Fabrizio; Oliveri, Giacomo; Rocca, Paolo; Massa, Andrea

doi:10.1109/eucap.2014.6902229

Cited by 19 publications

(27 citation statements)

References 15 publications

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“…The integration of various environmental sensors, such as temperature, humidity and light sensors, is capable of providing accurate and sufficient information related to occupancy in a building and public areas. 73 , 85 Khan et al 61 presented an analysis framework for occupancy estimation at different granularity levels for efficient building management. Similarly, Ebadat et al 74 evaluated the occupancy level by convoluting the environmental signal, which contained room temperature, HVAC actuation and CO 2 concentration.…”

Section: Conventional Occupancy Sensing Approachesmentioning

confidence: 99%

Occupancy detection systems for indoor environments: A survey of approaches and methods

Trivedi

Badarla

2019

Indoor and Built Environment

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Utilization of smart services for indoor environments has gained extensive interest in the past decade due to the resulting increases in energy savings, user comfort and degree of automation. Occupancy detection is a critical element in automation systems due to its potential use in controlling electrical systems and devices such as lighting, air-conditioning and ventilation. It also has a high potential for improving the performance of demand-driven applications which require fine-grained occupancy information to optimize the trade-off between energy consumption and user comfort. Occupancy detection has been researched using different estimation methods and communication technologies. However, it remains challenging to procure sensory data and to model the occupancy information accurately due to the limitations of hardware deployment and underlying cost. This paper reviews existing occupancy methods and applications, along with their underlying issues. It provides a comparative analysis of the strategies from the perspectives of cost, intrusiveness and accuracy. Additionally, a new taxonomy which classifies the occupancy sensing techniques as being conventional or as alternate sensing has been proposed.

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Section: Conventional Occupancy Sensing Approachesmentioning

confidence: 99%

Occupancy detection systems for indoor environments: A survey of approaches and methods

Trivedi

Badarla

2019

Indoor and Built Environment

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“…Once λ 0 in (20) has been found, then generalizing to other untrained rooms is immediate, as soon as one knows the relative room volumes v j .…”

Section: B Estimating λ Jmentioning

confidence: 99%

“…A novel approach is proposed in [16], where, using blind system identification techniques as in [17], no training sets including occupancy measurements are required. Other types of estimators use black box models identified using, e.g., neural networks or hidden Markov models, and potentially including several sources of information (e.g., temperature, humidity, concentrations, door statuses and light status, sound and motion, electricity consumption patterns) [18], [19], [20], [21], [22], [23], [24], [25]. This literature focuses on occupancy estimation in single rooms.…”

Section: Introductionmentioning

confidence: 99%

Multi-room occupancy estimation through adaptive gray-box models

Ebadat

Bottegal

Molinari

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We consider the problem of estimating the occupancy level in buildings using indirect information such as CO2 concentrations and ventilation levels. We assume that one of the rooms is temporarily equipped with a device measuring the occupancy. Using the collected data, we identify a gray-box model whose parameters carry information about the structural characteristics of the room. Exploiting the knowledge of the same type of structural characteristics of the other rooms in the building, we adjust the gray-box model to capture the CO2 dynamics of the other rooms. The occupancy estimators are then designed using a regularized deconvolution approach which aims at estimating the occupancy pattern that best explains the observed CO2 dynamics. We evaluate the proposed scheme through extensive simulation using a commercial software tool, IDA-ICE, for dynamic building simulation.

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“…Several systems are benefited by the occupancy information gathered in buildings, mainly the Heating, Ventilation, and Air Conditioners (HVAC) system. Similarly, real-time occupancy information can enable and benefit other building management services, such as security, emergency systems, fire systems, flow management, and automated energy management [ 5 , 8 , 9 , 10 ]. However, occupancy detection systems face several challenges, such as reliability, adaptability, and accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…The efforts to estimate occupancy in buildings can be classified into two categories: direct methods and indirect methods [ 8 ]. On the one hand, direct methods are based on technologies that can directly report the presence of human beings.…”

Section: Introductionmentioning

confidence: 99%

Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

Vela

Alvarado-Uribe

Davila

et al. 2020

Sensors

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The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3–4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%.

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Crowd detection and occupancy estimation through indirect environmental measurements

Abstract: International audienc

Cited by 19 publications

References 15 publications

Occupancy detection systems for indoor environments: A survey of approaches and methods

Occupancy detection systems for indoor environments: A survey of approaches and methods

Multi-room occupancy estimation through adaptive gray-box models

Estimating Occupancy Levels in Enclosed Spaces Using Environmental Variables: A Fitness Gym and Living Room as Evaluation Scenarios

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