Advanced approach for indoor identification and tracking using smart floor and pyroelectric infrared sensors

Ibrahim, Abdelhameed; Wong, Chi Biu; Moore, Philip; Chen, Xi

doi:10.1109/iacs.2014.6841966

Cited by 22 publications

(11 citation statements)

References 6 publications

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“…Thus, whenever floor sensors seemed to be insufficient for any of the three tasks (localisation, tracking and identification), additional sensors were used in a multimodal perspective to solve emerging data ambiguities. Sensing floors have been combined with radio-frequency identification (RFID) systems [29], pyroelectric infrared sensors [30], wearable accelerometers [31] [32], audio capture systems [33] and multiple cameras [34].…”

Section: Related Workmentioning

confidence: 99%

Localization of Humans, Objects, and Robots Interacting on Load-Sensing Floors

2016

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Localisation, tracking and recognition of objects and humans are basic tasks that are of high value in applications of ambient intelligence. Sensing floors were introduced to address these tasks in a non-intrusive way. To recognize the humans moving on the floor, they are usually first localized, and then a set of gait features are extracted (stride length, cadence, pressure profile over a footstep). However, recognition generally fails when several people stand or walk together, preventing successful tracking. This paper presents a detection, tracking and recognition technique which uses objects' weight. It continues working even when tracking individual persons becomes impossible. Inspired by computer vision, this technique processes the floor pressure-image by segmenting the blobs containing objects, tracking them, and recognizing their contents through a mix of inference and combinatorial search. The result lists the probabilities of assignments of known objects to observed blobs. The concept was successfully evaluated in daily life activity scenarii, involving multi-object tracking and recognition on low resolution sensors, crossing of user trajectories, and weight ambiguity. This technique can be used to provide a probabilistic input for multi-modal object tracking and recognition systems.

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Section: Related Workmentioning

confidence: 99%

Localization of Humans, Objects, and Robots Interacting on Load-Sensing Floors

2016

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“…A different field of application for smart floors, footboards and platforms is security [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. Herein, the floor is used to monitor the occupancy of surveilled or sensitive areas, possibly to identify the people present inside these areas and, finally, in the novel application field considered in this paper, for the control of access in walk-through gates.…”

Section: Introductionmentioning

confidence: 99%

Working Principle and Performance of a Scalable Gravimetric System for the Monitoring of Access to Public Places

Addabbo

Fort

Intravaia

et al. 2020

Sensors

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Here, we propose a novel application of a low-cost robust gravimetric system for public place access monitoring purposes. The proposed solution is intended to be exploited in a multi-sensor scenario, where heterogeneous information, coming from different sources (e.g., metal detectors and surveillance cameras), are collected in a central data fusion unit to obtain a more detailed and accurate evaluation of notable events. Specifically, the word “notable” refers essentially to two event categories: the first category is represented by irregular events, corresponding typically to multiple people passing together through a security gate; the second category includes some event subsets, whose notification can be interesting for assistance provision (in the case of people with disabilities), or for statistical analysis. The employed gravimetric sensor, compared to other devices existing in the literature, exhibits a simple scalable robust structure, made up of an array of rigid steel plates, each laid on four load cells. We developed a tailored hardware and software to individually acquire the load cell signals, and to post-process the data to formulate a classification of the notable events. The results are encouraging, showing a remarkable detectability of irregularities (95.3% of all the test cases) and a satisfactory identification of the other event types.

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“…Then, Kalman filtering and particle filtering were employed for target tracking. In [ 14 ], Al-Naimi presented an automatic identification and tracking method by combining data from PIR sensors and floor pressure sensors. In [ 15 ], a method using PIR and RF for localization was proposed to deal with the RSS variation issue that caused a significant localization error.…”

Section: Introductionmentioning

confidence: 99%

Passive Infrared (PIR)-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm

Yang

Bin

Rao

et al. 2018

Sensors

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Indoor occupants’ positions are significant for smart home service systems, which usually consist of robot service(s), appliance control and other intelligent applications. In this paper, an innovative localization method is proposed for tracking humans’ position in indoor environments based on passive infrared (PIR) sensors using an accessibility map and an A-star algorithm, aiming at providing intelligent services. First the accessibility map reflecting the visiting habits of the occupants is established through the integral training with indoor environments and other prior knowledge. Then the PIR sensors, which placement depends on the training results in the accessibility map, get the rough location information. For more precise positioning, the A-start algorithm is used to refine the localization, fused with the accessibility map and the PIR sensor data. Experiments were conducted in a mock apartment testbed. The ground truth data was obtained from an Opti-track system. The results demonstrate that the proposed method is able to track persons in a smart home environment and provide a solution for home robot localization.

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Advanced approach for indoor identification and tracking using smart floor and pyroelectric infrared sensors

Cited by 22 publications

References 6 publications

Localization of Humans, Objects, and Robots Interacting on Load-Sensing Floors

Localization of Humans, Objects, and Robots Interacting on Load-Sensing Floors

Working Principle and Performance of a Scalable Gravimetric System for the Monitoring of Access to Public Places

Passive Infrared (PIR)-Based Indoor Position Tracking for Smart Homes Using Accessibility Maps and A-Star Algorithm

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