A survey on multi person identification and localization

Ferdous, Shahina; Vyas, Kapil; Makedon, Fillia

doi:10.1145/2413097.2413143

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…For example, Crandall and Cook ( 2011 ) proposed an algorithmic approach for tracking data from multiple residents in a single home. Srinivasan et al ( 2010 ) used a height sensor installed in the home for resident identification and other groups have used wearables or video cameras (Wang et al, 2011 ; Banerjee et al, 2012 ; Ferdous et al, 2012 ). ORCATECH researchers are exploring using wearable technologies in several pilot studies.…”

Section: Limitationsmentioning

confidence: 99%

Pervasive Computing Technologies to Continuously Assess Alzheimerâ€™s Disease Progression and Intervention Efficacy

Lyons

Austin

Seelye

et al. 2015

Front. Aging Neurosci.

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Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals’ health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients’ and caregivers’ ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

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Section: Limitationsmentioning

confidence: 99%

Pervasive Computing Technologies to Continuously Assess Alzheimerâ€™s Disease Progression and Intervention Efficacy

Lyons

Austin

Seelye

et al. 2015

Front. Aging Neurosci.

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“…In recent years, the field of indoor localization has increased in popularity due to both the increasing number of applications [1] in domains such as surveillance [2], navigation (both assistive and general purpose) [3], robotics [4][5][6], and Augmented Reality (AR) [7] and the many proposed solutions that differ in terms of the devices used for tracking, the type of sensor data, and the localization algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

Morar

Moldoveanu

Mocanu

et al. 2020

Sensors

112

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Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).

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“…Under specified circumstances and requirements from applications, independent technologies were proposed and optimized [ 2 , 3 ]. Among the most used technologies are GPS, RFID, other methods based on WiFi, camera, accelerometer, microphones, and so forth [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

User Localization in Complex Environments by Multimodal Combination of GPS, WiFi, RFID, and Pedometer Technologies

Dao

Nguyen

Pham

et al. 2014

The Scientific World Journal

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Many user localization technologies and methods have been proposed for either indoor or outdoor environments. However, each technology has its own drawbacks. Recently, many researches and designs have been proposed to build a combination of multiple localization technologies system which can provide higher precision results and solve the limitation in each localization technology alone. In this paper, a conceptual design of a general localization platform using combination of multiple localization technologies is introduced. The combination is realized by dividing spaces into grid points. To demonstrate this platform, a system with GPS, RFID, WiFi, and pedometer technologies is established. Experiment results show that the accuracy and availability are improved in comparison with each technology individually.

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A survey on multi person identification and localization

Cited by 6 publications

References 15 publications

Pervasive Computing Technologies to Continuously Assess Alzheimerâ€™s Disease Progression and Intervention Efficacy

Pervasive Computing Technologies to Continuously Assess Alzheimerâ€™s Disease Progression and Intervention Efficacy

A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision

User Localization in Complex Environments by Multimodal Combination of GPS, WiFi, RFID, and Pedometer Technologies

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