Bluetooth-controlled Parking System Based on WiFi Positioning Technology

Chen, Hsin‐Chuan; Lin, Rong-San; Huang, Chia-Yu; Tian, Lidan; Su, Xining; Yu, Hui

doi:10.18494/sam3349

Cited by 2 publications

(2 citation statements)

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“…In the future, intelligent VR terminals can be combined with related algorithms, such as neural radiation field [42] (NeRF), to achieve more rapid construction of the semantic raster map. Other localization sources (e.g., audio [43], UWB [44], Bluetooth [45]) can also be introduced to assist in target tracking to improve reliability and robustness.…”

Section: Discussionmentioning

confidence: 99%

An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention

Hong

Luo³

et al. 2022

Remote Sensing

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Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer’s encoder–decoder structure. A multi-dimensional feature extraction backbone network was combined with a self-built raster semantic map which was stored in the encoder for correlation and generated target position encoding and multi-dimensional feature vectors. The decoder incorporated four methods: spatial clustering and semantic filtering of multi-view targets; dynamic matching of multi-dimensional features; space–time logic-based multi-target tracking, and space–time convergence network (STCN)-based parameter passing. Through the fusion of multiple decoding methods, multi-camera targets were tracked in three dimensions: temporal logic, spatial logic, and feature matching. For the MOT17 dataset, this study’s method significantly outperformed the current state-of-the-art method by 2.2% on the multiple object tracking accuracy (MOTA) metric. Furthermore, this study proposed a retrospective mechanism for the first time and adopted a reverse-order processing method to optimize the historical mislabeled targets for improving the identification F1-score (IDF1). For the self-built dataset OVIT-MOT01, the IDF1 improved from 0.948 to 0.967, and the multi-camera tracking accuracy (MCTA) improved from 0.878 to 0.909, which significantly improved the continuous tracking accuracy and reliability.

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Section: Discussionmentioning

confidence: 99%

An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention

Hong

Luo³

et al. 2022

Remote Sensing

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show abstract

“…Meanwhile, there is also literature on various improvements in terms of loss function, number of neurons, number of layers, etc., to improve the positioning accuracy. WIFI, Bluetooth, and inertial sensors, as the most common devices in people's lives, when used in wireless positioning can greatly reduce the cost of positioning [95]. How to effectively utilize these low-cost, easy-to-deploy devices to achieve higher-precision positioning is still a challenge many scholars need to overcome.…”

Section: Remaining Problems and Development Trends 71 Current Challengesmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization.

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Bluetooth-controlled Parking System Based on WiFi Positioning Technology

Cited by 2 publications

References 0 publications

An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention

An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention

Research Progress of Wireless Positioning Methods Based on RSSI

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