The Multi-Object and Segmentation (MOTS) benchmark 2 consists of 21 training sequences and 29 test sequences. It is based on the KITTI Tracking Evaluation 2012 and extends the annotations to the Multi-Object and Segmentation (MOTS) task. To this end, we added dense pixel-wise segmentation labels for every object. We evaluate submitted results using the metrics HOTA, CLEAR MOT, and MT/PT/ML. We rank methods by HOTA 1. (adapted for the segmentation case). Evaluation is performed using the code from the TrackEval repository. 1 J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: MOTS: Multi-Object Tracking and Segmentation. CVPR 2019.
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…It consists of 29 time-lapse image sequences with various annotations (pixel-wise segmentation masks, object-wise bounding boxes, and tracking information), made publicly available to the scientific community
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…We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking.
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…The cross-domain outdoor to indoor transition segments are especially challenging because of realistic sensor behavior such as GNSS degradation and dropouts, changes in the measured magnetic field, and flight scenario, such as the transition data, which requires sensor switching, or the Mars analog data with higher velocities, multiple touchdowns, challenging ground structures or constant velocity segments
…Squats Bird Dogs Supermans Bicycle Crunches Leg Raises Front Raises (with dumbbells) Overhead Press (with dumbbells) Annotations The dataset includes the following annotations: Bounding boxes Segmentation
…They include body height, weight, and segment lengths measured before the beginning of a recording session.