Automated people-counting by using low-resolution infrared and visual cameras

Amin, Imran; Taylor, Andrew J.; Junejo, Faraz; Al‐Habaibeh, Amin; Parkin, Robert M.

doi:10.1016/j.measurement.2007.02.010

Cited by 54 publications

(28 citation statements)

References 9 publications

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“…The accuracy of such approaches can vary according to the level of ambient lighting and background colour contrasts [6]. Hybrid approaches combining IR and video cameras, together with neural networks, have been proposed to improve the accuracy of visual-based automated people counting [7].…”

Section: A People Counting Methodsmentioning

confidence: 99%

Exploiting radio irregularity in the Internet of Things for automated people counting

Lin

Seah

Wei

2011

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications

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Abstract-The Internet of Things (IoT) is a new concept that refers to an Internet connecting not just computer systems but a plethora of systems, devices, and objects, collectively referred to as "Things", and encompasses technologies for identification and tracking, sensing and actuation, both wired and wireless communications, and also, intelligence and cognition. Wireless communications, which is an integral part of IoT, suffers from radio irregularity -a phenomenon referring to radio waves being selectively absorbed, reflected or scattered by objects in their paths, e.g., human bodies that comprises liquid, bone and flesh. Radio irregularity is often regarded as a problem in wireless communications but, with the envisioned pervasiveness of IoT, we aim to exploit radio irregularity as a means to detect people. We demonstrate how radio signal fluctuations arising from radio irregularity can be used to provide a low-cost alternative to dedicated sensing systems for indoor automated people counting.

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Section: A People Counting Methodsmentioning

confidence: 99%

Exploiting radio irregularity in the Internet of Things for automated people counting

Lin

Seah

Wei

2011

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications

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“…Other specialized human information sensors have also been developed for counting occupants (Amin et al 2008). Several kinds of sensors currently can provide information on occupancy, such as video cameras equipped with people-counting software, optical tripwires and pyroelectric infrared (PIR) motion sensors that count the number of people crossing a particular area, and sensors that measure the concentration of CO 2 in a space.…”

Section: Sensor Technologiesmentioning

confidence: 99%

“…With the human detection algorithm, data on occupancy can be detected with 97% accuracy (in 389 samples). Amin et al (2008) developed a system for counting people in a scene using a combination of low-cost, low-resolution visual and infrared cameras. The results of 18 experiments show that the maximum error percentage is within 3% over a wide range of lighting conditions.…”

Section: Thermal Image Systems Using Thermal Array Detectorsmentioning

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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“…However, this solution is not optimal as singular-beam counters do not provide directional counts; these sensors can detect only the total number of passengers crossing a predefined line, irrespective of their direction (Amin et al, 2008). In order to count passengers and determine their direction of movement, image processing techniques that utilise video data can be used (Chow and Cho, 2002;Hu et al, 2013Hu et al, , 2015Sacchi et al, 2001;Schofield et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Pedestrian monitoring techniques for crowd-flow prediction

Martani

StentSimon

AcikgozSinan

et al. 2017

Proceedings of the Institution of Civil Engineers - Smart Infra

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The high concentration and flow rate of people in train stations during rush hours can pose a prominent risk to passenger safety and comfort. In situ counting systems are a critical element for predicting pedestrian flows in real time, and their capabilities must be rigorously tested in live environments. The focus of this paper is on evaluating the reliability of two alternative counting systems, the first using an array of infrared depth sensors and the second a visible light (RGB) camera. Both proposed systems were installed at a busy walkway in London Bridge station. The data were collected over a period of 2 months, after which, portions of the data set were labelled for quantitative evaluation against ground truth. In this paper, the implementation of the two different counting technologies is described, and the accuracy and limitations of both approaches under different conditions are discussed. The results show that the developed RGB-based system performs reliably across a wide range of conditions, while the depth-based approach proves to be a useful complement in conditions without significant ambient sunlight, such as underground passageways.

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Automated people-counting by using low-resolution infrared and visual cameras

Cited by 54 publications

References 9 publications

Exploiting radio irregularity in the Internet of Things for automated people counting

Exploiting radio irregularity in the Internet of Things for automated people counting

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

Pedestrian monitoring techniques for crowd-flow prediction

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