Detection and tracking of a human using the infrared thermopile array sensor — “Grid-EYE”

Shetty, Akshaya D.; Disha,; B, Shubha; Suryanarayana, K.

doi:10.1109/icicict1.2017.8342790

Cited by 32 publications

(19 citation statements)

References 4 publications

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“…Shetty et al proposed tracking a subject using the foreground regions from the thermopile images [23]. Unfortunately, the authors did not report any accuracy values.…”

Section: A Localization Using Thermopile Sensorsmentioning

confidence: 99%

“…The model-based localization techniques proposed in the literature (e.g., [23], [27]) appear to be unable to accurately capture the complex relationship between IR data and the relative position of the target with respect to the sensors. The reported ML techniques (e.g., [28], [29]) largely adopt the fingerprinting approach.…”

Section: B Key Concept and Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired From Thermopiles and Machine Learning

et al. 2021

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The development of an accurate passive localization system utilizing thermopile sensing and artificial intelligence is discussed in this paper. Several machine learning techniques are explored to create robust angular and radius coordinate models for a localization target with respect to thermopile sensors. These models are leveraged to develop a reconfigurable passive localization system that can use a varying number of thermopiles without the need for retraining. The proposed robust system achieves high localization accuracy (with the median error between 0.13 m and 0.2 m) while being trained using a single human subject and tested against multiple other subjects. It is shown that the proposed system does not experience any significant performance deterioration when localizing a subject at different ambient temperatures or with different configurations of the thermopile sensors placement.INDEX TERMS Device-free localization (DFL), human sensing, indoor positioning system (IPS), infrared sensing, machine learning, passive localization, thermopile.

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“…Shetty et al proposed tracking a subject using the foreground regions from the thermopile images [23]. Unfortunately, the authors did not report any accuracy values.…”

Section: A Localization Using Thermopile Sensorsmentioning

confidence: 99%

Section: B Key Concept and Contributionmentioning

confidence: 99%

Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired From Thermopiles and Machine Learning

et al. 2021

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“…The existing proposals for smart lighting are mainly based on the use of presence detectors installed along the street [84], [85] or on each street lamp [86]. These devices basically consist of an infrared sensor that detects pedestrians [87], [88]. This type of technology is used by the most common commercial solutions around the world [89].…”

Section: Case Study a Application Definitionmentioning

confidence: 99%

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

et al. 2019

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New information and communication technologies have contributed to the development of the smart city concept. On a physical level, this paradigm is characterized by deploying a substantial number of different devices that can sense their surroundings and generate a large amount of data. The most typical case is image and video acquisition sensors. Recently, these types of sensors are found in abundance in urban spaces and are responsible for producing a large volume of multimedia data. The advanced computer vision methods for this type of multimedia information means that many aspects can be dynamically monitored, which can help implement value-added applications in the city. However, obtaining more elaborate semantic information from these data poses significant challenges related to a large amount of data generated and the processing capabilities required. This paper aims to address these issues by using a combination of cloud computing technologies and mobile computing techniques to design a three-layer distributed architecture for intensive urban computing. The approach consists of distributing the processing tasks among a city's multimedia acquisition devices, a middle computing layer, known as a cloudlet, and a cloud-computing infrastructure. As a result, each part of the architecture can now focus on a small number of tasks for which they are specially designed, and data transmission communication needs are significantly reduced. To this end, the cloud server can hold and centralize the multimedia analysis of the processed results from the lower layers. Finally, a case study on smart lighting is described to illustrate the benefits of using the proposed model in smart city environments. INDEX TERMS Mobile cloud computing, data processing, distributed architectures, smart city, urban computing.

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“…Detection and localization is described in References [47,48], which are texts on camera systems for finding objects in an image matrix; in References [49][50][51], the methods of locating objects/persons in the image are discussed. References [48,52] were used as sources in tracing, and text [2] was used as a source of information on tracing using calibration methods and inverse kinematics methods as another direction in dealing with the issue of tracing.…”

Section: Introductionmentioning

confidence: 99%

A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

Schneider

Macháček

Martínek

et al. 2020

Sensors

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This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated.

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Detection and tracking of a human using the infrared thermopile array sensor — “Grid-EYE”

Cited by 32 publications

References 4 publications

Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired From Thermopiles and Machine Learning

Device-Free Localization Using Privacy-Preserving Infrared Signatures Acquired From Thermopiles and Machine Learning

Distributed Architectures for Intensive Urban Computing: A Case Study on Smart Lighting for Sustainable Cities

A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study

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