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Dataset results
28 results for “depth sensor”
Spatio-thermal depth correction of RGB-D sensors based on Gaussian Processes in real-time
<p>This RGB-D dataset is part is part of our publication</p> <p>Heindl, Christoph, et al. "Spatio-thermal depth correction of RGB-D sensors based on Gaussian processes in real-time." <em>Tenth International Conference on Machine Vision (ICMV 2017)</em>. Vol. 10696. SPIE, 2018.</p> <p>Our capture setup consists of a RGB-D sensor looking towards a known planar object. The sensor is coupled with an electronic linear axis to adjust distance. We captured data at distances [40cm, 90cm, 10cm steps] in the temperate range of [25°C, 35°C, 1°C steps]. At each temperature/distance tuple we grabbed 50 images from both RGB and IR (aligned with RGB) sensors. We then created an artificial depth map for all RGB images utilizing the known calibration target in sight.</p> <p>For more information visit https://github.com/cheind/rgbd-correction</p>
Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor
<p>Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset. </p> <p>A sample of the dataset can be found here: <a href="https://doi.org/10.5281/zenodo.3457385">https://doi.org/10.5281/zenodo.3457385</a></p>
Neural Network Adaption for Depth Sensor Replication (Datasets)
<p>This repository contains the datasets used in the paper "Neural Network Adaption for Depth Sensor<br> Replication". They contain RGB-D data that was recorded using iPads where the Structure_Sensor set was recorded using an Occipital Structure Sensor while Apple_Lidar used the inbuilt iPad LiDAR sensor with enabled smoothing by ARKit. The Structure Sensor data is scaled so that the maximum value is 4 meters while the LiDAR data is scaled to 8 meters as it provides more accuracy at longer ranges.</p>
Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor [Sample Dataset]
<p>[Sample Dataset File.]</p> <p>Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset. </p>
Conductivity Temperature Depth (CTD) sensor profile data binned by depth from stations within the CCE region from CCE LTER P0904 student cruise, April 2009.
Data from deployed Seabird 911 CTD mounted on a 24-bottle rosette during P0904 student cruise in the CCE region. The CTD-rosette is lowered into the ocean (to depths up to 1000m) at selected hydrographic stations and multiple times across fronts, using the ship's conductive-wire winch. Data from many sensors are transmitted up the conductive wire and displayed real-time on a data aquistion computer. Discrete seawater samples are collected in 10L bottles at specific depths determined by the chlorophyll maximum and mixed layer depth. These samples are analyzed at sea and used to assess the CTD sensor data quality, plus measure additional properties. Processed CTD profile data are binned by depth and include: depth, temperature, salinity, density (sigma theta), oxygen, O2 saturation, PAR (radiation, surface radiation, and % irradiance), fluorescence, transmission, and nitrate. These data are then compared to the seawater sample data, and corrected if necessary.
Conductivity Temperature Depth (CTD) sensor profile data binned by depth from stations within the CCE region from CCE LTER process cruises, 2006 - 2017 (ongoing).
Since 2006 (ongoing), the CCE LTER program has deployed a Seabird 911 CTD mounted on a 24-bottle rosette during Process cruises in the CCE region. The CTD-rosette is lowered into the ocean (to depths up to 1000m) at selected hydrographic stations and multiple times across fronts, using the ship's conductive-wire winch. Data from many sensors are transmitted up the conductive wire and displayed real-time on a data aquistion computer. Discrete seawater samples are collected in 10L bottles at specific depths determined by the chlorophyll maximum and mixed layer depth. These samples are analyzed at sea and used to assess the CTD sensor data quality, plus measure additional properties. Processed CTD profile data are binned by depth and include: depth, temperature, salinity, density (sigma theta), oxygen, O2 saturation, PAR (radiation, surface radiation, and % irradiance), fluorescence, transmission, and nitrate. These data are then compared to the seawater sample data, and corrected if necessary.
Conductivity Temperature Depth (CTD) sensor profile data binned by depth from PAL LTER annual cruises, 1991 - 2017 (ongoing).
Since 1991 (and ongoing), the PAL LTER program has deployed a SeaBird 911+ CTD mounted on a 24-bottle rosette during annual (Austral Summer) cruises plus a few supplemental cruises at other times of the year. An equal area grid oriented parallel to the average coast provides the basis for sampling, as well as specific process studies and on-the-fly scientific needs. The CTD-rosette is lowered into the ocean (usually to just above the sea-floor) using the ship's conductive-wire winch. Data is collected and displayed real-time to ensure quality and make decisions about where to collect seawater with the bottles. Bottle data is typically collected extensively in the seasaonal mixed layer and pycnocline, plus at Tmin, in the permament pycnolcine and at Tmax and Smax, as well as near the bottom. Bottle data allows measurement adn calculation of additional variables and helps ensure quality data collected via sensors. Sensors include: Pressure, Conductivity (for Salinity), Temperature, Oxygen, Transmissometer, Flourometer, Photosynthetically Available Radiation (PAR/Irrandiance). Additional Bottle Data Variables include: Phosphate, Silicate, Nitrite, Nitrate, Ammonium. After each cruise, Temperature, Conductivity and Oxygen sensors are calbrated and post-crusie processing is applied, making use of pre- and post- cruise calibrations as well as SeaBird software and algorithms for getting the best quality data. Each profile is then inspected for any issues and if needed, suitable corrections are made such as using secondary sensors (temperature, conductivity and oxygen all currently measured in duplicate), using the upcast, or flagging the data as bad.
Usability of the ADEPTH Sensor for Bore Depth Measurements in Plate Osteosynthesis Procedures
ClinicalTrials.gov study NCT05960591. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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