Sökning: "indoor tracking"
Visar resultat 1 - 5 av 56 uppsatser innehållade orden indoor tracking.
1. Machine Learning for Spatial Positioning for XR Environments
Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : This bachelor's thesis explores the integration of machine learning (ML) with sensor fusion techniques to enhance spatial data accuracy in Extended Reality (XR) environments. With XR's revolutionary impact across various sectors, accurate localization in virtual environments becomes imperative. LÄS MER
2. Wi-Fi fingerprinting as a mean to measure building occupancy : A case study in an office environment
Master-uppsats, KTH/TransportplaneringSammanfattning : The task of collecting visitor data in an indoor environment and therein determining the occupancy of a building is an extensive task. Conventional methods are expensive, time-consuming, and often lack the ability to produce data in longer time series. Further, they often require disruption of the studied area as equipment must be deployed. LÄS MER
3. Object Recognition and Tracking of Bolts: A Comparative Analysis of CNN Models and Computer Vision Techniques : A Comparison of CNN Models and Tracking Algorithms
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The newer generation industry 4.0 focuses on development of both flexibility and autonomy for power tools used by companies in different mechanical areas and assembly lines. One area for automation is the application of computer vision in power tools to detect, identify and track bolts. LÄS MER
4. Map generation using a smartphone’s built in localisation and mapping algorithms
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : Localisation and mapping algorithms for smartphones have in recent years seen a renewed interest due to their technological advancements. Using these systems to generate an indoor blueprint where none is available or incomplete is useful in tracking applications. LÄS MER
5. Comparison of camera data types for AI tracking of humans in indoor combat training
Master-uppsats, Jönköping University/JTH, Avdelningen för datavetenskapSammanfattning : Multiple object tracking (MOT) can be an efficient tool for finding patterns in video monitoring data. In this thesis, we investigate which type of video data works best for MOT in an indoor combat training scenario. The three types of camera data evaluated are color data, near-infrared (NIR) data, and depth data. LÄS MER