A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar

Lin, Junyu; Hu, Jun; Xie, Zhiyuan; Zhang, Yulan; Huang, Guangjia; Chen, Zengping

doi:10.3390/s23198147

Cited by 4 publications

(2 citation statements)

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“…Several works have explored detecting or tracking people using mmWave radar [ 7 , 16 , 18 , 20 , 21 ]. The work in [ 7 ] presents an identification system named mID, utilizing mmWave radar technology.…”

Section: Introductionmentioning

confidence: 99%

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

Mattos,

Brante,

Moritz

et al. 2024

Sensors

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Millimeter-wave (mmWave) radars attain high resolution without compromising privacy while being unaffected by environmental factors such as rain, dust, and fog. This study explores the challenges of using mmWave radars for the simultaneous detection of people and small animals, a critical concern in applications like indoor wireless energy transfer systems. This work proposes innovative methodologies for enhancing detection accuracy and overcoming the inherent difficulties posed by differences in target size and volume. In particular, we explore two distinct positioning scenarios that involve up to four mmWave radars in an indoor environment to detect and track both humans and small animals. We compare the outcomes achieved through the implementation of three distinct data-fusion methods. It was shown that using a single radar without the application of a tracking algorithm resulted in a sensitivity of 46.1%. However, this sensitivity significantly increased to 97.10% upon utilizing four radars using with the optimal fusion method and tracking. This improvement highlights the effectiveness of employing multiple radars together with data fusion techniques, significantly enhancing sensitivity and reliability in target detection.

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Section: Introductionmentioning

confidence: 99%

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

Mattos,

Brante,

Moritz

et al. 2024

Sensors

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“…Lightweight crowd-counting models often implement techniques such as model pruning [1], parameter sharing [2,3], model quantization [4], and knowledge distillation [5] to reduce parameters and computation cost. Sun et al [4] utilized model quantization in their model for a microcontroller unit (MCU), representing a 2.2× speedup compared to the original float model, which indicates its effectiveness in resource-constrained devices.…”

Section: Introductionmentioning

confidence: 99%

A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

Chen,

Zhao,

Gao

et al. 2024

Electronics

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Crowd-counting networks have become the mainstream method to deploy crowd-counting techniques on resource-constrained devices. Significant progress has been made in this field, with many outstanding lightweight models being proposed successively. However, challenges like scare-variation, global feature extraction, and fine-grained head annotation requirements still exist in relevant tasks, necessitating further improvement. In this article, we propose a weakly supervised hybrid lightweight crowd-counting network that integrates the initial layers of GhostNet as the backbone to efficiently extract local features and enrich intermediate features. The incorporation of a modified Swin-Transformer block addresses the need for effective global context information. A Pyramid Pooling Aggregation Module handles the inherent scale variation problem in crowd-counting tasks in a more computation-efficient way. This module, along with the cross-attention module, serves as bridges to promote the feature information flow between local features and global context information. Finally, a simplified regressor module is designed to allow the proposed model with weakly supervised guidance for training to avoid precise location-level annotations, noting that the omission of density map generation makes the proposed network more lightweight. Our results on the UCF-QNRF dataset indicate our model is 8.73% and 12.17% more accurate on MAE and MSE metrics, respectively, than the second-best ARNet, in which the parameters decrease by 4.52%. On the ShanghaiTech A dataset, MAE and MSE drop 1.5% and 3.2%, respectively, compared to the second-best PDDNet. The experimental results for accuracy and inference speed evaluation on some mainstream datasets validate the effective design principle of our model.

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Multi-function vortex array radar

Li,

Bashiri,

Ponomarenko

et al. 2024

Applied Physics Letters

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In the realm of automation systems, multi-function radars serve as essential sensory components for self-driving vehicles and airbornes. Effective resource allocation management is crucial, requiring a high level of versatility to accomplish multiple tasks, especially, for increasingly miniaturized hardware. Here, we advance a balanced protocol for detecting, positioning, and tracking moving targets in real-time. Our protocol integrates efficient data processing methods with robust hardware. Specifically, detection signals are modulated by optical vortices for imaging, and real time processing of the image field facilitates target positioning and tracking. Moreover, the protocol extends its utility to serve as a topographic laser profiling system for natural landscapes, highlighting its adaptability. This adaptability and versatility well position the proposed protocol to support a wide range of applications, spanning self-driving vehicles and aerial systems, underscoring its potential significance across multiple platforms.

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A Multitask Network for People Counting, Motion Recognition, and Localization Using Through-Wall Radar

Cited by 4 publications

References 38 publications

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications

A Weakly Supervised Hybrid Lightweight Network for Efficient Crowd Counting

Multi-function vortex array radar

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