SCOPES: Smart Cameras Object Position Estimation System

Kamthe, Ankur; Jiang, Lun; Dudys, Matthew; Cerpa, Alberto E.

doi:10.1007/978-3-642-00224-3_18

Cited by 53 publications

(46 citation statements)

References 10 publications

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“…The University of California deploys a wireless camera sensor network [57] for collecting data regarding occupancy in a large multi-function building. Using data collected from this system, they construct multivariate Gaussian and agent-based models for predicting user mobility patterns in buildings.…”

Section: The Office Sectormentioning

confidence: 99%

Energy intelligent buildings based on user activity: A survey

Nguyen

Aiello

2013

Energy and Buildings

604

297

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a b s t r a c tOccupant presence and behaviour in buildings has been shown to have large impact on heating, cooling and ventilation demand, energy consumption of lighting and appliances, and building controls. Energyunaware behaviour can add one-third to a building's designed energy performance. Consequently, user activity and behaviour is considered as a key element and has long been used for control of various devices such as artificial light, heating, ventilation, and air conditioning. However, how are user activity and behaviour taken into account? What are the most valuable activities or behaviours and what is their impact on energy saving potential? In order to answer these questions, we provide a novel survey of prominent international intelligent buildings research efforts with the theme of energy saving and user activity recognition. We devise new metrics to compare the existing studies. Through the survey, we determine the most valuable activities and behaviours and their impact on energy saving potential for each of the three main subsystems, i.e., HVAC, light, and plug loads. The most promising and appropriate activity recognition technologies and approaches are discussed thus allowing us to conclude with principles and perspectives for energy intelligent buildings based on user activity.

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Section: The Office Sectormentioning

confidence: 99%

Energy intelligent buildings based on user activity: A survey

Nguyen

Aiello

2013

Energy and Buildings

604

297

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“…The key points (e.g., joints) are computed locally while the posture is estimated at the base station. There is also recent work in using camera networks for object tracking [17,12]. However their focus is different.…”

Section: Related Workmentioning

confidence: 99%

“…However their focus is different. In [17], the tracking system makes a localised decision to switch between simple or elaborate background subtraction methods according to the application requirements to decrease resource consumption of camera networks. However, [17] does not propose any new background subtraction methods whereas our work proposes a new background subtraction method which is both accurate and computational efficient.…”

Section: Related Workmentioning

confidence: 99%

Efficient background subtraction for real-time tracking in embedded camera networks

Shen

Liu

et al. 2012

Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems

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Background subtraction is often the first step of many computer vision applications. For a background subtraction method to be useful in embedded camera networks, it must be both accurate and computationally efficient because of the resource constraints on embedded platforms. This makes many traditional background subtraction algorithms unsuitable for embedded platforms because they use complex statistical models to handle subtle illumination changes. These models make them accurate but the computational requirement of these complex models is often too high for embedded platforms. In this paper, we propose a new background subtraction method which is both accurate and computational efficient. The key idea is to use compressive sensing to reduce the dimensionality of the data while retaining most of the information. By using multiple datasets, we show that the accuracy of our proposed background subtraction method is comparable to that of the traditional background subtraction methods. Moreover, real implementation on an embedded camera platform shows that our proposed method is at least 5 times faster, and consumes significantly less energy and memory resources than the conventional approaches. Finally, we demonstrated the feasibility of the proposed method by the implementation and evaluation of an end-to-end real-time embedded camera network target tracking application.

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“…Such events can lead to a significant positive bias (Hashimoto et al 1997a(Hashimoto et al , 1997b(Hashimoto et al , 1998(Hashimoto et al , 2000Meyn et al 2009). Kamthe et al (2009) developed the SCOPES system, a wireless camera sensor network for gathering traces of human mobility patterns in buildings. The claimed accuracy is 80% of events counted correctly.…”

Section: Sensor Technologiesmentioning

confidence: 99%

Review of Literature on Terminal Box Control, Occupancy Sensing Technology and Multi-zone Demand Control Ventilation (DCV)

Liu¹,

Dasu²,

Zhang³

2012

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Executive summaryThis report presents the results of an analysis of published studies and other literature concerned with terminal box control, occupancy sensing technology, and multi-zone demand control ventilation (DCV) in commercial buildings. To meet the ventilation needs of building occupants, heating, ventilating and airconditioning systems provide outdoor air. For many systems, air is brought into a commercial building through air-handling units, which supply conditioned air to many thermal zones in the building. The airhandling units mix outdoor air in a controlled proportion with recirculated air and then cool the mixture before distribution to the terminal boxes. Terminal boxes usually serve a single building zone, controlling the air flow rate to the zone and reheating the air, if it is too cool for the zone served. Each terminal box has a minimum air flow rate set so that it meets the ventilation requirements of the occupants of the zone the box serves. This minimum air flow rate is commonly designed as a constant value based on the design occupancy of the zone served, which usually corresponds to the maximum occupancy.In practice, control system integrators and installers often set the minimum air flow rate for ventilation to between 30% and 50% of the terminal box maximum air flow rate. Building occupancy, however, varies dynamically. Conference rooms, cafeterias, break rooms, auditoriums, and other assembly spaces are often unoccupied for significant periods of time. Office occupancy varies during the course of a work day, from day to day, and over the longer term because of meetings in the office, attendance of meetings elsewhere, business travel, changing room functions during remodeling, and variations in staffing. The resulting over-ventilation, during times when the space has less than maximum occupancy or is unoccupied, wastes significant fan power, resulting in energy waste, and even causing discomfort for occupants in some spaces (e.g., conference rooms) from overcooling.Carbon-dioxide-based demand control ventilation has existed for a number of years; however, as currently used, it controls outdoor-air intake by the air handler only, not ventilation of individual zones or rooms. The need for frequent recalibration (e.g., annually) and the high cost of installation for each zone (or room) makes CO 2 -based control of ventilation at terminal boxes impractical. A multi-lab report (Brambley et al. 2005) for the U.S. Department of Energy (DOE) on advanced sensors and controls identifies a need for "development of sensors to determine occupancy number and population distribution within buildings." There are no such terminal unit controllers on the market today to modulate air flow to zones based on actual occupancy. The Advanced Energy Retrofit Guide (AERG) for Office Buildings, sponsored by DOE's Building Technology Program (BTP), reveals that a 4% reduction in whole-building energy consumption can be achieved by implementing occupancy-based control (OBC) in conference rooms alone ).This repor...

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SCOPES: Smart Cameras Object Position Estimation System

Cited by 53 publications

References 10 publications

Energy intelligent buildings based on user activity: A survey

Energy intelligent buildings based on user activity: A survey

Efficient background subtraction for real-time tracking in embedded camera networks

Review of Literature on Terminal Box Control, Occupancy Sensing Technology and Multi-zone Demand Control Ventilation (DCV)

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