DeepSportradar is a benchmark suite of computer vision tasks, datasets and benchmarks for automated sport understanding. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided.
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A large synthetic multi-camera crowd counting dataset with a large number of scenes and camera views to capture many possible variations, which avoids the difficulty of collecting and annotating such a large real dataset.
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CVGL Camera Calibration Dataset consists of 49 camera configurations with town 1 having 25 configurations while town 2 having 24 configurations. The parameters modified for generating the configurations include fov, x, y, z, pitch, yaw, and roll. Here, fov is the field of view, (x, y, z) is the translation while (pitch, yaw, and roll) is the rotation between the cameras. The total number of image pairs is 79, 320, out of which 18, 083 belong to Town 1 while 61, 237 belong to Town 2, the difference in the number of images is due to the length of the tracks.
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