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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Video object segmentation has been studied extensively in the past decade due to its importance in understanding video spatial-temporal structures as well as its value in industrial applications. Previously, we presented the first large-scale video object segmentation dataset named YouTubeVOS and hosted the Large-scale Video Object Segmentation Challenge in conjuction with ECCV 2018, ICCV 2019 This year, we are thrilled to invite you to the 4th Large-scale Video Object Segmentation Challenge in conjunction with CVPR 2022.
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…Note that this implies TAO-Amodal also includes modal segmentation masks (as visualized in the color overlays above).
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…synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation
120 PAPERS • 1 BENCHMARK
…We benchmark four foundational video understanding tasks: action recognition, action segmentation, object detection and multi-object tracking.