3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition
An inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occl...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/8735846 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850234914842083328 |
|---|---|
| author | Binbin Wang Tingli Su Xuebo Jin Jianlei Kong Yuting Bai |
| author_facet | Binbin Wang Tingli Su Xuebo Jin Jianlei Kong Yuting Bai |
| author_sort | Binbin Wang |
| collection | DOAJ |
| description | An inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments, such as those involving fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an optimal gait recognition algorithm to improve the accuracy of gait detection. Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. According to the recognition results, the position change in the vertical direction could be reasonably corrected. The obtained 3D trajectory involving both horizontal and vertical movements has shown that the accuracy is significantly improved in practical complex environments. |
| format | Article |
| id | doaj-art-18442a9d532c4fc2bfbbfb4294ee2c1b |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-18442a9d532c4fc2bfbbfb4294ee2c1b2025-08-20T02:02:29ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/873584687358463D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait RecognitionBinbin Wang0Tingli Su1Xuebo Jin2Jianlei Kong3Yuting Bai4School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, ChinaSchool of Automation, Beijing Institute of Technology, Beijing 100081, ChinaAn inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments, such as those involving fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an optimal gait recognition algorithm to improve the accuracy of gait detection. Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. According to the recognition results, the position change in the vertical direction could be reasonably corrected. The obtained 3D trajectory involving both horizontal and vertical movements has shown that the accuracy is significantly improved in practical complex environments.http://dx.doi.org/10.1155/2018/8735846 |
| spellingShingle | Binbin Wang Tingli Su Xuebo Jin Jianlei Kong Yuting Bai 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition Complexity |
| title | 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition |
| title_full | 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition |
| title_fullStr | 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition |
| title_full_unstemmed | 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition |
| title_short | 3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition |
| title_sort | 3d reconstruction of pedestrian trajectory with moving direction learning and optimal gait recognition |
| url | http://dx.doi.org/10.1155/2018/8735846 |
| work_keys_str_mv | AT binbinwang 3dreconstructionofpedestriantrajectorywithmovingdirectionlearningandoptimalgaitrecognition AT tinglisu 3dreconstructionofpedestriantrajectorywithmovingdirectionlearningandoptimalgaitrecognition AT xuebojin 3dreconstructionofpedestriantrajectorywithmovingdirectionlearningandoptimalgaitrecognition AT jianleikong 3dreconstructionofpedestriantrajectorywithmovingdirectionlearningandoptimalgaitrecognition AT yutingbai 3dreconstructionofpedestriantrajectorywithmovingdirectionlearningandoptimalgaitrecognition |