Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation
In the past two decades, object tracking has progressively advanced in computer vision and image processing. Tracking is a collection of algorithms that detect and track objects in a video sequence. This has resulted in a broad variety of applications, including surveillance, biometric identificatio...
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| Format: | Conference or Workshop Item |
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2023
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| _version_ | 1817854144256409600 |
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| author | Ajasa, Abiodun Afis Wahyudi, Nawawi Sophan |
| author_facet | Ajasa, Abiodun Afis Wahyudi, Nawawi Sophan |
| author_sort | Ajasa, Abiodun Afis |
| collection | ePrints |
| description | In the past two decades, object tracking has progressively advanced in computer vision and image processing. Tracking is a collection of algorithms that detect and track objects in a video sequence. This has resulted in a broad variety of applications, including surveillance, biometric identification or biological imaging, human-machine interactions, traffic control, and intelligent vehicles, all of which have benefited from tracking applications. This paper presents an IoT-based human tracking system that employs the Kalman filter (KF) and extended Kalman filter (EKF) algorithms. However, the performance of the filters is impacted by noise. Therefore, instead of manually tuning the KF and EKF’s process noise and measurement error, particle swarm optimisation (PSO) is utilised to optimise them. Four 2-dimensional models (namely conventional KF and EKF, optimised KF-PSO, and EKF-PSO) were developed and evaluated using ten distinct human sets of data containing 100 samples each. The object’s tracked positions are estimated in horizontal and vertical directions. Accuracy analysis was used to compare the four models’ quality performance. With an average mean square error of 5.99 mm (or 0.599%), the EKF-PSO model outperformed the conventional EKF model, which had an error of 7.18 mm (or 0.718%). A 17.2 mm (or 1.72%) error was found in the KF-PSO model. The last one is the traditional KF model, yielding an error of 22.4 mm (or 2.24%). As a result of its higher accuracy, the EKF-PSO model outperforms the other three models. |
| first_indexed | 2024-12-08T06:53:28Z |
| format | Conference or Workshop Item |
| id | utm.eprints-107646 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-12-08T06:53:28Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | utm.eprints-1076462024-09-25T07:34:48Z http://eprints.utm.my/107646/ Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation Ajasa, Abiodun Afis Wahyudi, Nawawi Sophan TK Electrical engineering. Electronics Nuclear engineering In the past two decades, object tracking has progressively advanced in computer vision and image processing. Tracking is a collection of algorithms that detect and track objects in a video sequence. This has resulted in a broad variety of applications, including surveillance, biometric identification or biological imaging, human-machine interactions, traffic control, and intelligent vehicles, all of which have benefited from tracking applications. This paper presents an IoT-based human tracking system that employs the Kalman filter (KF) and extended Kalman filter (EKF) algorithms. However, the performance of the filters is impacted by noise. Therefore, instead of manually tuning the KF and EKF’s process noise and measurement error, particle swarm optimisation (PSO) is utilised to optimise them. Four 2-dimensional models (namely conventional KF and EKF, optimised KF-PSO, and EKF-PSO) were developed and evaluated using ten distinct human sets of data containing 100 samples each. The object’s tracked positions are estimated in horizontal and vertical directions. Accuracy analysis was used to compare the four models’ quality performance. With an average mean square error of 5.99 mm (or 0.599%), the EKF-PSO model outperformed the conventional EKF model, which had an error of 7.18 mm (or 0.718%). A 17.2 mm (or 1.72%) error was found in the KF-PSO model. The last one is the traditional KF model, yielding an error of 22.4 mm (or 2.24%). As a result of its higher accuracy, the EKF-PSO model outperforms the other three models. 2023 Conference or Workshop Item PeerReviewed Ajasa, Abiodun Afis and Wahyudi, Nawawi Sophan (2023) Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation. In: Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2022, 20 July 2022, Pekan, Pahang, Malaysia. http://dx.doi.org/10.1007/978-981-19-8703-8_19 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Ajasa, Abiodun Afis Wahyudi, Nawawi Sophan Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation |
| title | Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation |
| title_full | Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation |
| title_fullStr | Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation |
| title_full_unstemmed | Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation |
| title_short | Performance comparison of Kalman Filter and extended kalman filter for human tracking and prediction with particle swarm optimisation |
| title_sort | performance comparison of kalman filter and extended kalman filter for human tracking and prediction with particle swarm optimisation |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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