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1	Conditional Random People: Tracking Humans with CRFs and Grid Filters Taycher, 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. AI articulated tracking grid filter conditional random field
2	Application development of 3D LiDAR sensor for display computers Ekstrand, 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. LiDAR 3D sensor display computer application high-resolution 3D map visualize real time data CPU load Voxel grid filter Point Cloud Library Qt QML C++ Linux LiDAR 3D sensor fordons dator applikation högupplöst 3D-karta visualisera realtidsdata processorlast Voxel grid filter Point Cloud Library Qt QML C++ Linux Embedded Systems Inbäddad systemteknik

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Conditional Random People: Tracking Humans with CRFs and Grid Filters

Application development of 3D LiDAR sensor for display computers