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The efforts to create a non-trivial and publicly available dataset for action recognition was initiated at the KTH Royal Institute of Technology in 2004. The KTH dataset is one of the most standard datasets, which contains six actions: walk, jog, run, box, hand-wave, and hand clap. To account for performance nuance, each action is performed by 25 different individuals, and the setting is systematically altered for each action per actor. Setting variations include: outdoor (s1), outdoor with scale variation (s2), outdoor with different clothes (s3), and indoor (s4). These variations test the ability of each algorithm to identify actions independent of the background, appearance of the actors, and the scale of the actors. Source: Review of Action Recognition and Detection Methods

The KTH-TIPS (Textures under varying Illumination, Pose and Scale) image database was created to extend the CUReT database in two directions, by providing variations in scale as well as pose and illumination, and by imaging other samples of a subset of its materials in different settings. The KTH-TIPS2 databases took this a step further by imaging 4 different samples of 11 materials, each under varying pose, illumination and scale. For more information about the databases, please view the documentation. The databases may be downloaded here.

KTI Multiview Football II consists of images of professional footballers during a match of the Allsvenskan league. It consists of two parts: one with ground truth pose in 2D and one with ground truth pose in both 2D and 3D. The 3D dataset has 800 time frames, captured from 3 views (2400 images). Views are calibrated and synchronized. 3D ground truth pose and orthographic camera matrices are provided for each frame. There are 14 annotated joints. Lastly, there are 2 different players and two sequences per player. Source: http://www.csc.kth.se/~vahidk/football_data.html Image Source: http://www.csc.kth.se/cvap/cvg/?page=footballdataset2

This dataset was created as part of the following study, which was published in the Journal of Hydrology: A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area https://doi.org/10.1016/j.jhydrol.2021.126903. The pre-print is available on arXiv: https://arxiv.org/pdf/2105.05539.pdf Files description This dataset contains 4148 simulation results, i.e., 4148 pairs of predictor/target. bkt.npy contains the breakthrough curves from all 6 injection wells recorded at the pumping well. pz.npy contains the 2D coordinates of the backtracked particles' end points, used to delineate the WHPA. Introduction The Wellhead Protection Area (WHPA) is a zone around a pumping well where human activities are limited in order to preserve water resources, usually based on how long dangerous chemicals in the area will take to reach the pumping well (according to local regulation). The flow velocity in the subsurface around the well determines it, and it can be computed numerically using particle tracking or transport simulation, or in practice using tracer testing. A groundwater model is typically calibrated against field data before being used to calculate the WHPA. In highly populated places where land occupation is a big concern, the introduction of such zones could have a large socioeconomic impact. WHPA prediction Different tracers emerge from six data sources (injection wells) scattered across the pumping well. Their job is to inject individual tracers into the system in order to predict their transport and record their breakthrough curves (BCs) at the pumping well location. Numerous particles are artificially positioned around the pumping well, and their origins are traced backward in time to identify the associated WHPA. Our predictor and target will be generated using the USGS' open-source finite-difference code Modflow. To get different sets of predictors and targets, we will run different hydrologic models with one variable parameter, namely hydraulic conductivity in metres per day. To obtain a satisfactory heterogeneity in the hydraulic conductivity fields, which will control the shape and extent of our target, the PAs, we use sequential gaussian simulation based on arbitrarily defined variograms. The pumping well is located at the 1000, 500 metres mark and is surrounded by six injection wells.

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KTI Multiview Football I is a dataset of football players with annotated joints that can be used for multi-view reconstruction. The dataset includes 771 images of football players, images taken from 3 views at 257 time instances, and 14 annotated body joints. Source: http://www.csc.kth.se/~vahidk/football_data.html Image Source: http://www.csc.kth.se/~vahidk/football_data.html

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