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.
26 PAPERS • 1 BENCHMARK
BURST is a benchmark suite built upon TAO that requires tracking and segmenting multiple objects from camera video. Class-guided Common: Track and segment all objects belonging to a set of 78 common classes (based on the COCO class set) Long-tail: Track and segment all objects belonging to an extended set of 482 object all 482 object classes (class label predictions are not required) Exemplar-guided Mask: Track and segment all objects in the video for which the first-frame object masks are given. This task is identical to Video Object Segmentation (VOS). Box: Track and segment all objects in the video for which the first-frame object bounding-boxes are given. Point: Track and segment all objects in the video for which we are only given the (x,y) point coordinates of the mask centroid in the first-frame in which the objects appear.
14 PAPERS • 5 BENCHMARKS