Dissertation/ Thesis
Systems and Methods for Multiple-View and Depth-Based People Tracking and Human-Computer Interaction ; Syvyyskameroihin ja usean kameran hyödyntämiseen perustuvia järjestelmiä ja menetelmiä ihmisten seurantaan sekä ihmisen ja tietokoneen väliseen vuorovaikutukseen
| العنوان: | Systems and Methods for Multiple-View and Depth-Based People Tracking and Human-Computer Interaction ; Syvyyskameroihin ja usean kameran hyödyntämiseen perustuvia järjestelmiä ja menetelmiä ihmisten seurantaan sekä ihmisen ja tietokoneen väliseen vuorovaikutukseen |
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| المؤلفون: | Korkalo, Otto |
| المساهمون: | Takala, Tapio, Prof. Emer., Aalto University, Department of Computer Science, Finland, Perustieteiden korkeakoulu, School of Science, Tietotekniikan laitos, Department of Computer Science, Kannala, Juho, Prof. Aalto University, Department of Computer Science, Finland, Aalto-yliopisto, Aalto University |
| بيانات النشر: | Aalto University Aalto-yliopisto |
| سنة النشر: | 2024 |
| المجموعة: | Aalto University Publication Archive (Aaltodoc) / Aalto-yliopiston julkaisuarkistoa |
| مصطلحات موضوعية: | Computer science, depth cameras, multiple-view systems, people tracking, camera pose estimation, multi-touch systems, mixed reality, syvyyskamerat, monikamerajärjestelmät, ihmisten seuranta, kameran paikan ja asennon estimointi, monikosketusnäytöt, yhdistetty todellisuus |
| الوصف: | This thesis presents systems and methods for real-time multiple-view and depth-based optical tracking for specific human-computer interaction and smart environment applications. Multiple-view systems are used for mitigating occlusions, enhancing tracking precision and accuracy, and extending the tracking volume to encompass larger scales. Depth cameras, on the other hand, offer the advantage of directly providing three-dimensional information from the scene, which makes them particularly appealing for spatial analysis. For multi-touch interaction, we developed a tracking approach that utilizes multiple side-view cameras to transform any flat surface into a multi-touch screen. Instead of explicitly triangulating the touch points, we employed an extended Kalman filter-based method in which the states of the touch points are updated whenever an observation is received from any of the cameras, ensuring low latency and rapid update rates. To position the cameras as close to the screen as possible, we employed fisheye lenses with modified distortion model, and explored the optimal camera configuration for achieving robust tracking with varying numbers of cameras and touch points. Accurate intrinsic and extrinsic calibration of cameras and camera systems is essential for optimal data fusion and state estimation. Typically, calibration procedures are carried out manually, which is not only time-consuming but can also be impractical. To address this issue in multiple-view depth camera-based people tracking systems, we have developed an auto-calibration method that directly derives the camera network topology and sensor calibration parameters from observations. Additionally, to account for the uncertainties in the observations during state estimation and data fusion, we developed a measurement noise model as part of the auto-calibration procedure. In mixed reality, the aim of camera pose estimation and tracking is to align the real and virtual environments in real-time and in all three dimensions. To achieve this goal, we ... |
| نوع الوثيقة: | doctoral or postdoctoral thesis |
| وصف الملف: | 77 + app. 75; application/pdf |
| اللغة: | English |
| ردمك: | 978-952-64-1788-2 978-952-64-1787-5 952-64-1788-7 952-64-1787-9 |
| Relation: | Aalto University publication series DOCTORAL THESES; 88/2024; [Publication 1]: Otto Korkalo and Petri Honkamaa. Construction and Evaluation of Multi-Touch Screens Using Multiple Cameras Located on the Side of the Display. In ACM International Conference on Interactive Tabletops and Surfaces, Saarbrücken, Germany, pp. 83–90, November 2010. DOI:10.1145/1936652.1936667; [Publication 2]: Otto Korkalo, Tommi Tikkanen, Paul Kemppi and Petri Honkamaa. Auto-Calibration of Depth Camera Networks for People Tracking. Machine Vision and Applications, 30(4):671–688, 2019. DOI:10.1007/s00138-019-01021-z; [Publication 3]: Otto Korkalo and Tapio Takala. Measurement Noise Model for Depth Camera-Based People Tracking. Sensors, 21(13):4488, 2021. Full text in Acris/Aaltodoc: https://urn.fi/URN:NBN:fi:aalto-202108048081. DOI:10.3390/s21134488; [Publication 4]: Otto Korkalo and Svenja Kahn. Real-time Depth Camera Tracking with CAD Models and ICP. Journal of Virtual Reality and Broadcasting, 13(2016):1, 2016. DOI:10.20385/1860-2037/13.2016.1; 1799-4942 (electronic); 1799-4934 (printed); 1799-4934 (ISSN-L); https://aaltodoc.aalto.fi/handle/123456789/127390; URN:ISBN:978-952-64-1788-2 |
| الاتاحة: | https://aaltodoc.aalto.fi/handle/123456789/127390 |
| رقم الانضمام: | edsbas.4E3C3E47 |
| قاعدة البيانات: | BASE |
| ردمك: | 9789526417882 9789526417875 9526417887 9526417879 |
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