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Conditional Random People: Tracking Humans with CRFs and Grid FiltersTaycher, Leonid, Shakhnarovich, Gregory, Demirdjian, David, Darrell, Trevor 01 December 2005 (has links)
We describe a state-space tracking approach based on a Conditional Random Field(CRF) model, where the observation potentials are \emph{learned} from data. Wefind functions that embed both state and observation into a space wheresimilarity corresponds to $L_1$ distance, and define an observation potentialbased on distance in this space. This potential is extremely fast to compute and in conjunction with a grid-filtering framework can be used to reduce acontinuous state estimation problem to a discrete one. We show how a statetemporal prior in the grid-filter can be computed in a manner similar to asparse HMM, resulting in real-time system performance. The resulting system isused for human pose tracking in video sequences.
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Application development of 3D LiDAR sensor for display computersEkstrand, Oskar January 2023 (has links)
A highly accurate sensor for measuring distances, used for creating high-resolution 3D maps of the environment, utilize “Light Detection And Ranging” (LiDAR) technology. This degree project aims to investigate the implementation of 3D LiDAR sensors into off-highway vehicle display computers, called CCpilots. This involves a study of available low-cost 3D LiDAR sensors on the market and development of an application for visualizing real time data graphically, with room for optimization algorithms. The selected LiDAR sensor is “Livox Mid-360”, a hybrid-solid technology and a field of view of 360° horizontally and 59° vertically. The LiDAR application was developed using Livox SDK2 combined with a C++ back-end, in order to visualize data using Qt QML as the Graphical User Interface design tool. A filter was utilized from the Point Cloud Library (PCL), called a voxel grid filter, for optimization purpose. Real time 3D LiDAR sensor data was graphically visualized on the display computer CCpilot X900. The voxel grid filter had a few visual advantages, although it consumed more processor power compared to when no filter was used. Whether a filter was used or not, all points generated by the LiDAR sensor could be processed and visualized by the developed application without any latency.
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