Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction
Motivation. The visual tracking of patients with specific adverse conditions such as epileptic seizures is an important task related to the prevention of unwanted medical situations and events. Previously, we have developed algorithms for contactless patient tracking based on optical flow analysis....
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MDPI AG
2024-12-01
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| author | Simeon Karpuzov George Petkov Stiliyan Kalitzin |
| author_facet | Simeon Karpuzov George Petkov Stiliyan Kalitzin |
| author_sort | Simeon Karpuzov |
| collection | DOAJ |
| description | Motivation. The visual tracking of patients with specific adverse conditions such as epileptic seizures is an important task related to the prevention of unwanted medical situations and events. Previously, we have developed algorithms for contactless patient tracking based on optical flow analysis. In this work, we address some of the challenges faced by the single-camera tracking system and expand its functionalities by employing simultaneous input from multiple cameras. Methods. We propose a new approach of fusing multiple camera sensors. It uses a proprietary motion-group parameter reconstruction algorithm and includes scenarios of both overlapping and non-overlapping fields of view. In the first case, simultaneous tracking within the overlapping field evolves from independent tracking by each camera to synchronized tracking by a set of cameras. This is achieved by automated reinforcement learning and simultaneously applying the interdependences between the cameras. In addition, outside the overlapping areas, the algorithm can transfer tracking from one camera to another, provided a tree-type topology between the areas is present. Results. We demonstrate that synchronous, multi-camera tracking scenarios provide improvements in both real-world and simulated tests. This new approach allows for improving the accuracy and robustness of the original methods, to extend the tracking coverage, and to provide other beneficial effects, such as more precise detection of fast-moving objects. The proposed method is compared with other algorithms used in the field. |
| format | Article |
| id | doaj-art-fd9072553b3e47759b5f459d4eebcef0 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-fd9072553b3e47759b5f459d4eebcef02025-01-24T13:35:06ZengMDPI AGInformation2078-24892024-12-01161410.3390/info16010004Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter ReconstructionSimeon Karpuzov0George Petkov1Stiliyan Kalitzin2GATE Institute, Sofia University, 1164 Sofia, BulgariaGATE Institute, Sofia University, 1164 Sofia, BulgariaStichting Epilepsie Instellingen Nederland (SEIN), Achterweg 5, 2103 SW Heemstede, The NetherlandsMotivation. The visual tracking of patients with specific adverse conditions such as epileptic seizures is an important task related to the prevention of unwanted medical situations and events. Previously, we have developed algorithms for contactless patient tracking based on optical flow analysis. In this work, we address some of the challenges faced by the single-camera tracking system and expand its functionalities by employing simultaneous input from multiple cameras. Methods. We propose a new approach of fusing multiple camera sensors. It uses a proprietary motion-group parameter reconstruction algorithm and includes scenarios of both overlapping and non-overlapping fields of view. In the first case, simultaneous tracking within the overlapping field evolves from independent tracking by each camera to synchronized tracking by a set of cameras. This is achieved by automated reinforcement learning and simultaneously applying the interdependences between the cameras. In addition, outside the overlapping areas, the algorithm can transfer tracking from one camera to another, provided a tree-type topology between the areas is present. Results. We demonstrate that synchronous, multi-camera tracking scenarios provide improvements in both real-world and simulated tests. This new approach allows for improving the accuracy and robustness of the original methods, to extend the tracking coverage, and to provide other beneficial effects, such as more precise detection of fast-moving objects. The proposed method is compared with other algorithms used in the field.https://www.mdpi.com/2078-2489/16/1/4epilepsytrackingoptic flowmulti-camera |
| spellingShingle | Simeon Karpuzov George Petkov Stiliyan Kalitzin Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction Information epilepsy tracking optic flow multi-camera |
| title | Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction |
| title_full | Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction |
| title_fullStr | Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction |
| title_full_unstemmed | Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction |
| title_short | Multiple-Camera Patient Tracking Method Based on Motion-Group Parameter Reconstruction |
| title_sort | multiple camera patient tracking method based on motion group parameter reconstruction |
| topic | epilepsy tracking optic flow multi-camera |
| url | https://www.mdpi.com/2078-2489/16/1/4 |
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