Abstract
Object tracking is a critical task that finds its applications in various fields including surveillance and autonomous robots. However, most of the work on object tracking has been developed on images and video data. In contrast, the aim of our work is to develop reliable object tracking system based on sequence of measurements which can be obtained from radio sensors, that are more suitable for privacy-concerned applications. In addition, we propose to use linear regression, in contrast to complex data-driven models, to demonstrate its performance against conventional tracking algorithm i.e., particle filter. Our experimental results show that LR can predict a moving object’s position with minimal error and significantly outperforms the particle filter by more than 90%. All the experiments have been validated via simulations.
Original language | English |
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Title of host publication | Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1 |
Editors | Andrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang |
Publisher | Springer, Cham |
Pages | 25-35 |
Number of pages | 11 |
Volume | 151 |
ISBN (Electronic) | 9783031494130 |
ISBN (Print) | 9783031494123, 9783031494154 |
DOIs | |
Publication status | Published - 30 May 2024 |
Event | The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom Duration: 29 Aug 2023 → 1 Sep 2023 https://unified2023.org/ |
Publication series
Name | Mechanisms and Machine Science |
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Publisher | Springer |
Volume | 151 MMS |
ISSN (Print) | 2211-0984 |
ISSN (Electronic) | 2211-0992 |
Conference
Conference | The UNIfied Conference of DAMAS, InCoME and TEPEN Conferences |
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Abbreviated title | UNIfied 2023 |
Country/Territory | United Kingdom |
City | Huddersfield |
Period | 29/08/23 → 1/09/23 |
Internet address |