Research on Tracking Foreign Objects in Railway Tracks Based on Hidden Markov Kalman Filter Article Swipe
Tao Hou
,
Zhao Yanzhang
,
Hongxia Niu
,
Mingxi Chen
,
Shan Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.6180/jase.202210_25(5).0020
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.6180/jase.202210_25(5).0020
The intrusion of foreign object on the railway tracks directly affects the safe operation of the trains. Due to the complex railway tracks environment, the existing research on foreign object detection based on image processing has problems such as weak anti-noise, poor real-time performance, and low accuracy. A real-time detection and tracking method for foreign bodies invading railway tracks based on Hidden Markov Model (HMM) Kalman Filter is put forward in this paper. Firstly, the Gaussian Mixture Model is used to extract the feature vector of the object in multiple images and generate a feature sequence. Secondly, the feature sequence of the detected object is processed by the Hidden Markov Model, and the movement railway tracks of the foreign object is predicted. Finally, the prediction result is compared with the actual results. The Kalman filter is updated according to the comparison results, and the foreign objects invading the railway tracks are finally detected and tracked. The simulation results show that the method can detect accurately and quickly and track foreign objects invading the railway tracks. Compared with the existing foreign object detection results achieved by the application of neural networks and Gaussian Mixture Models, the processing and results of the algorithm have strong anti-noise performance and real-time performance. It has high definition and an accuracy rate of 98.73%, which can further ensure the safety of train operation.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doaj.org/article/61ea0f5c2f0544159e7a9ff8bc34fe9a
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386993858
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386993858Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.6180/jase.202210_25(5).0020Digital Object Identifier
- Title
-
Research on Tracking Foreign Objects in Railway Tracks Based on Hidden Markov Kalman FilterWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-01Full publication date if available
- Authors
-
Tao Hou, Zhao Yanzhang, Hongxia Niu, Mingxi Chen, Shan WangList of authors in order
- Landing page
-
https://doaj.org/article/61ea0f5c2f0544159e7a9ff8bc34fe9aPublisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doaj.org/article/61ea0f5c2f0544159e7a9ff8bc34fe9aDirect OA link when available
- Concepts
-
Kalman filter, Tracking (education), Hidden Markov model, Computer science, Artificial intelligence, Computer vision, Psychology, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2022: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Gaussian | 75, 191 |
| abstract_inverted_index.accuracy | 214 |
| abstract_inverted_index.achieved | 183 |
| abstract_inverted_index.compared | 127 |
| abstract_inverted_index.detected | 102, 152 |
| abstract_inverted_index.directly | 9 |
| abstract_inverted_index.existing | 25, 178 |
| abstract_inverted_index.generate | 92 |
| abstract_inverted_index.invading | 56, 146, 171 |
| abstract_inverted_index.movement | 113 |
| abstract_inverted_index.multiple | 89 |
| abstract_inverted_index.networks | 189 |
| abstract_inverted_index.problems | 36 |
| abstract_inverted_index.research | 26 |
| abstract_inverted_index.results, | 141 |
| abstract_inverted_index.results. | 131 |
| abstract_inverted_index.sequence | 99 |
| abstract_inverted_index.tracked. | 154 |
| abstract_inverted_index.tracking | 51 |
| abstract_inverted_index.Secondly, | 96 |
| abstract_inverted_index.according | 137 |
| abstract_inverted_index.accuracy. | 46 |
| abstract_inverted_index.algorithm | 200 |
| abstract_inverted_index.detection | 30, 49, 181 |
| abstract_inverted_index.intrusion | 1 |
| abstract_inverted_index.operation | 13 |
| abstract_inverted_index.processed | 105 |
| abstract_inverted_index.real-time | 42, 48, 206 |
| abstract_inverted_index.sequence. | 95 |
| abstract_inverted_index.accurately | 164 |
| abstract_inverted_index.anti-noise | 203 |
| abstract_inverted_index.comparison | 140 |
| abstract_inverted_index.definition | 211 |
| abstract_inverted_index.operation. | 226 |
| abstract_inverted_index.predicted. | 121 |
| abstract_inverted_index.prediction | 124 |
| abstract_inverted_index.processing | 34, 195 |
| abstract_inverted_index.simulation | 156 |
| abstract_inverted_index.anti-noise, | 40 |
| abstract_inverted_index.application | 186 |
| abstract_inverted_index.performance | 204 |
| abstract_inverted_index.environment, | 23 |
| abstract_inverted_index.performance, | 43 |
| abstract_inverted_index.performance. | 207 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.63437629 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |