Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequatel...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/1/568 |
| _version_ | 1797626212335484928 |
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| author | Shaoze You Hua Zhu Menggang Li Yutan Li Chaoquan Tang |
| author_facet | Shaoze You Hua Zhu Menggang Li Yutan Li Chaoquan Tang |
| author_sort | Shaoze You |
| collection | DOAJ |
| description | In recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker’s performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments. |
| first_indexed | 2024-03-11T10:07:14Z |
| format | Article |
| id | doaj.art-cd43ab8c849d4e2aab30915fdfad1abe |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:07:14Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-cd43ab8c849d4e2aab30915fdfad1abe2023-11-16T14:58:49ZengMDPI AGApplied Sciences2076-34172022-12-0113156810.3390/app13010568Tracking System for a Coal Mine Drilling Robot for Low-Illumination EnvironmentsShaoze You0Hua Zhu1Menggang Li2Yutan Li3Chaoquan Tang4School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaIn recent years, discriminative correlation filters (DCF) based trackers have been widely used in mobile robots due to their efficiency. However, underground coal mines are typically a low illumination environment, and tracking in this environment is a challenging problem that has not been adequately addressed in the literature. Thus, this paper proposes a Low-illumination Long-term Correlation Tracker (LLCT) and designs a visual tracking system for coal mine drilling robots. A low-illumination tracking framework combining image enhancement strategies and long-time tracking is proposed. A long-term memory correlation filter tracker with an interval update strategy is utilized. In addition, a local area illumination detection method is proposed to prevent the failure of the enhancement algorithm due to local over-exposure. A convenient image enhancement method is proposed to boost efficiency. Extensive experiments on popular object tracking benchmark datasets demonstrate that the proposed tracker significantly outperforms the baseline trackers, achieving high real-time performance. The tracker’s performance is verified on an underground drilling robot in a coal mine. The results of the field experiment demonstrate that the performance of the novel tracking framework is better than that of state-of-the-art trackers in low-illumination environments.https://www.mdpi.com/2076-3417/13/1/568visual object trackinglow illuminationimage enhancementcomputer visionmobile drilling robotcoal mine robot |
| spellingShingle | Shaoze You Hua Zhu Menggang Li Yutan Li Chaoquan Tang Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments Applied Sciences visual object tracking low illumination image enhancement computer vision mobile drilling robot coal mine robot |
| title | Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments |
| title_full | Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments |
| title_fullStr | Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments |
| title_full_unstemmed | Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments |
| title_short | Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments |
| title_sort | tracking system for a coal mine drilling robot for low illumination environments |
| topic | visual object tracking low illumination image enhancement computer vision mobile drilling robot coal mine robot |
| url | https://www.mdpi.com/2076-3417/13/1/568 |
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