Multi-Camera Human Activity Monitoring

Fiore, L.; Fehr, Duc; Bodor, Robot; Drenner, Andrew; Somasundaram, Guruprasad; Papanikolopoulos, Nikolaos

doi:10.1007/s10846-007-9201-6

Cited by 82 publications

(43 citation statements)

References 44 publications

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“…In order to recognize multiple activities at the same location, we can assume to utilize a camera [9], [10], a wearable sensor, or a smartphone [11], [12].…”

Section: Motion-recognition-based Activity Recognitionmentioning

confidence: 99%

“…Hoey et al [9] estimate a user's activity by utilizing an image processing technique and partially observable Markov decision process. Fiore et al [10] estimate the activity of a user who walks inside the building by utilizing cameras and an image processing technique. However, the installing cost of the cameras, which the user has to set up in every room in the building and cables which connect between the cameras and the processing server becomes burden on the user.…”

Section: Motion-recognition-based Activity Recognitionmentioning

confidence: 99%

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Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Fujiwara

Kashimoto

Fujimoto

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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: These days, smart home applications such as concierge service for residents, home appliance control, and so on are attracting attention. To realize these applications, we need a system which recognizes various human activities accurately with a low cost device. There are many studies on the activity recognition in a smart home. We also have proposed an activity recognition technique in a smart home by utilizing digital-output-PIR (passive infrared) sensors, door sensors, and power meters. However, the study has an unsolved issue: we cannot distinguish similar activities happening at the same place, for example, "eating" and "reading" while sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the different activities in the same place. Our technique recognizes user's activity by utilizing machine learning with frequency components of the sensor's output as features. However, because the number of features used in ALPAS is 1000 for each analog PIR-sensor, a large capacity memory is required. To reduce the number of features, we select a part of the sensing data. We call the starting point of the selected data as starting frequency (SF) and ending part as ending frequency (EF). We searched SF and EF using a grid search, and evaluated the recognition accuracy. We evaluated the proposed technique in a smarthome testbed. In the evaluation, five participants performed four different activities while sitting on a sofa. As a result, we achieved F-Measure: 63.9% when the EF is 1.4 Hz, and F-Measure: 50% or lower when the SF is 9.9 Hz or higher.

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“…In order to recognize multiple activities at the same location, we can assume to utilize a camera [9], [10], a wearable sensor, or a smartphone [11], [12].…”

Section: Motion-recognition-based Activity Recognitionmentioning

confidence: 99%

Section: Motion-recognition-based Activity Recognitionmentioning

confidence: 99%

Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Fujiwara

Kashimoto

Fujimoto

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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“…Vision-based activity recognition uses visual sensing facilities, e.g., camera-based surveillance systems, to monitor an actor's behavior and its environment changes [3] [4] [5] [6]. It exploits computer vision techniques to analyse visual observations for pattern recognition.…”

Section: Activity Recognition Approachesmentioning

confidence: 99%

Activity Recognition: Approaches, Practices and Trends

Chen

Khalil

2011

Activity Recognition in Pervasive Intelligent Environments

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Activity recognition has attracted increasing attention as a number of related research areas such as pervasive computing, intelligent environments and robotics converge on this critical issue. It is also driven by growing real-world application needs in such areas as ambient assisted living and security surveillance. This chapter aims to provide an overview on existing approaches, current practices and future trends on activity recognition. It is intended to provide the necessary material to inform relevant research communities of the latest developments in this field in addition to providing a reference for researchers and system developers who are working towards the design and development of activity-based context aware applications. The chapter first reviews the existing approaches and algorithms that have been used for activity recognition in a number of related areas. It then describes the practice and lifecycle of the ontology-based approach to activity recognition that has recently been under vigorous investigation. Finally the chapter presents emerging research on activity recognition by outlining various issues and directions the field will take. IntroductionWith the advance and prevalence of low-cost low-power sensors, computing devices and wireless communication networks, pervasive computing has evolved from a vision [1] to an achievable and deployable computing paradigm. As a result, research is now being conducted in all areas related to pervasive computing, ranging from low-level data collection, to intermediate-level information processing, to high-level applications and service delivery. It is becoming increasingly evident that intelligent environments which can support both living and work places through flexible multimodal interactions, proactive service provision, and context aware personalized activity assistance will be commonplace in the very near future. For example, Smart Homes (SH) -augmented real or simulated home settings equipped with sensors, actuators and information processing systems, have been extensively studied. Work in this area has produced a number of lab-based or real-world SH prototypes [2]. Within a SH the Activities of Daily Living (ADL) of its inhabitants, usually 1

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“…The increase in the number of elderly people demands safety-monitoring systems that can obviate these concerns by detecting the posture of the elderly. Existing solutions for indoor use, such as networked video cameras [1] and wearable sensors [2,3], employ IoT (Internet of things) devices. However, the former is an invasion of privacy, particularly in spaces such as the bathroom and restroom.…”

Section: Introductionmentioning

confidence: 99%

Human Posture Identification Using a MIMO Array

et al. 2018

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Abstract:The elderly are constantly in danger of falling and injuring themselves without anyone realizing it. A safety-monitoring system based on microwaves can ease these concerns. The authors have proposed safety-monitoring systems that use multiple-input multiple-output (MIMO) radar to localize persons by capturing their biological activities such as respiration. However, our studies to date have focused on localization, which is easier to achieve than an estimation of human postures. This paper proposes a human posture identification scheme based on height and a Doppler radar cross section (RCS) as estimated by a MIMO array. This scheme allows smart home applications to dispense with contact and wearable devices. Experiments demonstrate that this method can identify the supine position (i.e., after a fall) with 100% accuracy, and the average identification rate is 95.0%.

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Multi-Camera Human Activity Monitoring

Cited by 82 publications

References 44 publications

Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Activity Recognition: Approaches, Practices and Trends

Human Posture Identification Using a MIMO Array

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