Computer Vision
The UBFC-Phys dataset is a public multimodal dataset dedicated to psychophysiological studies. 56 participants followed a three-step experience where they lived social stress through a rest task T1, a speech task T2 and an arithmetic task T3. During the experience, the participants were filmed and were wearing a wristband that measured their Blood Volume Pulse (BVP) and ElectroDermal Activity (EDA) signals. Before the experience started and once it finished, the participants filled a form allowing to compute their self-reported anxiety scores.
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For the task of detecting casualties and persons in search and rescue scenarios in drone images and videos, our database called SARD was built. The actors in the footage have simulate exhausted and injured persons as well as "classic" types of movement of people in nature, such as running, walking, standing, sitting, or lying down. Since different types of terrain and backgrounds determine possible events and scenarios in captured images and videos, the shots include persons on macadam roads, in quarries, low and high grass, forest shade, and the like.
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The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. In this dataset, we present different images and videos for computer vision-based research. The dataset comprises images and videos taken from different sources such as a Drone, a DSLR camera, and a mobile phone camera.
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Indirect hand measurement processes have been used to improve remote accessibility and non-contact acquisition methods. This is particularly helpful when developing custom products, such as prostheses or gloves, to a user. Indirect hand measurements, however, may be difficult to acquire due to the requirement that certain specifications to be met. In the case of indirect measurement determination from 3D scans, obstructions may affect the observed outcome. This is especially true when using low-cost 3D scanners that have not been optimized for medical use.
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We propose a camera calibration method to generate a high-quality and photorealistic 3D (dimension) volumetric graphics model using several low-cost commercial RGB-D (depth) cameras located in a limited space. We show an efficient workflow to register a model efficiently and propose iterative calibration techniques to construct it. Using multiple frames, calibration in the vertical direction between the upper and lower cameras is performed. After selecting any four pairs, the calibration is performed while rotating with the vertical calibration results from other adjacent viewpoints.
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The data and codes show 3D reconstruction of a checkerboard with pseudo-colored errors, using different calibration and reconstruction methods. (1) with general stereo calibration and linear reconstruction, (2) with general stereo calibration and approximately undistorted reconstruction, (3) with stereo calibration and undistorted reconstruction using nonlinear epipolar constraints, and (4) with the residual distortion-calibrated reconstruction.
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The boring and repetitive task of monitoring video feeds makes real-time anomaly detection tasks difficult for humans. Hence, crimes are usually detected hours or days after the occurrence. To mitigate this, the research community proposes the use of a deep learning-based anomaly detection model (ADM) for automating the monitoring process.
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The 3DLSC-COVID datset includes a total of 1,805 3D chest CT scans with more than 570,000 CT slices were collected from 2 standard CT scanners of Liyuan Hospital, i.e., UIH uCT 510 and GE Optima CT600. Among all CT scans, there were 794 positive cases of COVID-19, which were further confirmed by clinical symptoms and RT-PCR from January 16 to April 16, 2020.
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The current dataset – crowdbot – presents outdoor pedestrian tracking from onboard sensors on a personal mobility robot navigating in crowds. The robot Qolo, a personal mobility vehicle for people with lower-body impairments was equipped with a reactive navigation control operating in shared-control or autonomous mode when navigating on three different streets of the city of Lausanne, Switzerland during farmer’s market days and Christmas market. Full Dataset here: DOI:10.21227/ak77-d722
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YonseiStressImageDatabase is a database built for image-based stress recognition research. We designed an experimental scenario consisting of steps that cause or do not cause stress; Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview. And during the experiment, the subjects were photographed with Kinect v2. We cannot disclose the original image due to privacy issues, so we release feature maps obtained by passing through the network.
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