Heat-Map Based Occupancy Estimation Using Adaptive Boosting

Naser, Abdallah; Lotfi, Ahmad; Zhong, Junpei; He, Jifeng

doi:10.1109/fuzz48607.2020.9177685

Cited by 13 publications

(8 citation statements)

References 14 publications

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“…The use of TSA sensors in human-centred applications is relatively low compared to wearable, vision-based, and ambient sensors [15]. Conventional wearable devices use an accelerator's sensor to measure the object acceleration [16], [17], [18].…”

Section: Related Workmentioning

confidence: 99%

“…where u x , v y indicate the velocity of the frame in the horizontal and vertical directions, respectively, and the derivatives of x and y over time t are represented by I x , I y , I t . Hence, (15) has two unknowns, u and v, which can be solved using various mathematical methods. For instance, in the Horn and Schunck (global optical flow estimation) algorithm [36], the velocity estimation between consecutive motion frames is based on two assumptions: brightness and smoothness.…”

Section: Motion Feature Extractionmentioning

confidence: 99%

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Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

Naser

Lotfi

Mwanje

et al. 2023

IEEE Trans. Human-Mach. Syst.

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To support the independent living of older adults in their own homes, it is essential to identify their abnormal behaviors before triggering an automated alert system. Existing normal vision sensing approaches to detect human falls in the activities of daily living (ADL) experienced acceptability issues due to outstanding privacy concerns when they are deployed in personal environments. Besides, false alerts (false-positive) fall detection has not been addressed thoroughly in systems that report abnormal human behaviors as emergency alerts to the information support. This article proposes a novel human-in-the-loop fall detection approach in the ADLs using a low-resolution thermal sensor array. The motivation for enabling a human interactive model, fall detection confirmation, is to influence resource efficiency by reducing false-positive alerts while keeping the false-negative fall predictions as low as possible. The proposed approach is based on the motion sequence classification of human movements using a recurrent neural network. The proposed approach is evaluated with comprehensive experiments using different learning techniques, users, and domestic environment conditions. This article shows a performance accuracy of 99.7% to detect human falls from various typical ADLs.

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Section: Related Workmentioning

confidence: 99%

Section: Motion Feature Extractionmentioning

confidence: 99%

Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

Naser

Lotfi

Mwanje

et al. 2023

IEEE Trans. Human-Mach. Syst.

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“…Similarly, there has been recent works on using the TSA on human activity recognition, and abnormal behaviour detection [32]- [36]. The approach followed to process the TSA output is similar to image-processing approaches [37] while the analytical techniques on individual time intervals, frames, were different for instance Support Vector Machine (SVM) [12], Adaptive Boosting [6], [10], K-Nearest Neighbour (KNN) [30], [38], decision trees [20], [39], and Kalman filtering [40], [41]. One of the notable technical challenges reported in most human-based applications, which use TSA is that external heat sources have a major negative impact on the system performance.…”

Section: Related Workmentioning

confidence: 99%

Multiple Thermal Sensor Array Fusion Toward Enabling Privacy-Preserving Human Monitoring Applications

Naser

Lotfi

Zhong

2022

IEEE Internet Things J.

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Human-centric applications of a single Thermal Sensor Array (TSA) have performed extremely well in many areas. However, most of these works have not yet reached the real applicability stage of the Internet of Things (IoT) applications. The main limitation of deploying such systems on a large scale is the challenge of fusing multiple TSAs to cover a wide inspection area, e.g. smart homes, hospitals and many other domestic environments. On the other hand, objects that appear in the low-resolution thermal images acquired from TSA have low intra-class variations and high inter-class similarities, making the identification of the overlapping regions through matching a comparable template image in multiple images very difficult. This paper proposes a motion-based approach to fuse multiple TSAs and learn the domestic environment layout to enable further human-centred IoT applications to run in the cloud. Besides, a privacy-improvement on utilising these sensors in IoT applications is proposed. The proposed approach is evaluated with comprehensive experiments on different sensor placements and domestic environment conditions. This paper shows an average performance of 92.5% accuracy using various machine learning techniques and use case scenarios.

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“…Sensor-wise: sensors that use bio-metric information, such as cameras, sometimes can be replaced with other sensors such as LiDAR and infrared sensors [159][160][161]. If the bio-metric details of users must be captured, such details should be processed locally at the edge and should not be sent online.…”

Section: Solutionmentioning

confidence: 99%

On the Gap between Domestic Robotic Applications and Computational Intelligence

et al. 2021

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Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.

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Heat-Map Based Occupancy Estimation Using Adaptive Boosting

Cited by 13 publications

References 14 publications

Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

Privacy-Preserving, Thermal Vision With Human in the Loop Fall Detection Alert System

Multiple Thermal Sensor Array Fusion Toward Enabling Privacy-Preserving Human Monitoring Applications

On the Gap between Domestic Robotic Applications and Computational Intelligence

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