WiLHPE: WiFi-enabled Lightweight Channel Frequency Dynamic Convolution for HPE Tasks
Gian, Toan D.; Nguyen, Tien Hoa; Nguyen, Nhan Thanh; Nguyen, Van Dinh (2024-08-21)
IEEE
21.08.2024
T. D. Gian, T. -H. Nguyen, N. T. Nguyen and V. -D. Nguyen, "WiLHPE: WiFi-enabled Lightweight Channel Frequency Dynamic Convolution for HPE Tasks," 2024 Tenth International Conference on Communications and Electronics (ICCE), Danang, Vietnam, 2024, pp. 516-521, doi: 10.1109/ICCE62051.2024.10634628
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202412097102
https://urn.fi/URN:NBN:fi:oulu-202412097102
Tiivistelmä
Abstract
Recently, there has been significant attention to WiFi-based human pose estimation (HPE) within the research community due to its device-free nature, cost-effectiveness, and privacy preservation. The implementation of such a solution requires improved model performance while upholding efficiency, particularly when employing resource-constrained devices. To address these challenges, this paper introduces a novel approach, the so-called WiLHPE, which integrates multi-modal sensors such as cameras and WiFi to accurately detect human pose landmarks. WiLHPE involves processing the raw WiFi signal through a novel neural network architecture to dynamically learn convolutional kernels weighted with attention across channel and frequency kernel spaces. This innovative approach diversifies the kernels to enhance the recognition capabilities of WiFi signals without introducing additional complexity, thus guaranteeing efficiency. Results conducted on the MM-Fi dataset underscore the superiority of WiLHPE over state-of-the-art approaches, all while ensuring minimal computational overhead. This makes the proposed approach highly suitable for large-scale scenarios.
Recently, there has been significant attention to WiFi-based human pose estimation (HPE) within the research community due to its device-free nature, cost-effectiveness, and privacy preservation. The implementation of such a solution requires improved model performance while upholding efficiency, particularly when employing resource-constrained devices. To address these challenges, this paper introduces a novel approach, the so-called WiLHPE, which integrates multi-modal sensors such as cameras and WiFi to accurately detect human pose landmarks. WiLHPE involves processing the raw WiFi signal through a novel neural network architecture to dynamically learn convolutional kernels weighted with attention across channel and frequency kernel spaces. This innovative approach diversifies the kernels to enhance the recognition capabilities of WiFi signals without introducing additional complexity, thus guaranteeing efficiency. Results conducted on the MM-Fi dataset underscore the superiority of WiLHPE over state-of-the-art approaches, all while ensuring minimal computational overhead. This makes the proposed approach highly suitable for large-scale scenarios.
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