A Machine Learning Approach for Reliable Object Tracking

Bisma Amjad, Qasim Z. Ahmed, Pavlos Lazaridis, Faheem Khan, Maryam Hafeez, Zaharias D. Zaharis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationProceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 1
EditorsAndrew D. Ball, Huajiang Ouyang, Jyoti K. Sinha, Zuolu Wang
PublisherSpringer, Cham
Number of pages11
ISBN (Electronic)9783031494130
ISBN (Print)9783031494123, 9783031494154
Publication statusPublished - 30 May 2024
EventThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences - Huddersfield, United Kingdom, Huddersfield, United Kingdom
Duration: 29 Aug 20231 Sep 2023

Publication series

NameMechanisms and Machine Science
Volume151 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992


ConferenceThe UNIfied Conference of DAMAS, InCoME and TEPEN Conferences
Abbreviated titleUNIfied 2023
Country/TerritoryUnited Kingdom
Internet address

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