Towards human distance estimation using a thermal sensor array
| Autor: | Junpei Zhong, Ahmad Lotfi, Abdallah Naser |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science Field of view 02 engineering and technology 01 natural sciences Measure (mathematics) Special issue on Human-in-the-loop Machine Learning and its Applications Artificial Intelligence Adaptive system 0202 electrical engineering electronic engineering information engineering Computer vision business.industry Social distance 010401 analytical chemistry Estimator Semantic segmentation Distance estimation 0104 chemical sciences Thermal sensor array Human-centred approach Transmission (telecommunications) Feature (computer vision) 020201 artificial intelligence & image processing Artificial intelligence business Software |
| Zdroj: | Neural Computing & Applications |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of$$\pm 0.2$$±0.2 m in continuous-based estimation and$$96.8\%$$96.8%achieved-accuracy in discrete distance estimation. |
| Databáze: | OpenAIRE |
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