STEP: Segmenting and Tracking Every Pixel

• URL: http://arxiv.org/abs/2102.11859v1
• Date: Tue, 23 Feb 2021 18:43:02 GMT
• Title: STEP: Segmenting and Tracking Every Pixel
• Authors: Mark Weber, Jun Xie, Maxwell Collins, Yukun Zhu, Paul Voigtlaender, Hartwig Adam, Bradley Green, Andreas Geiger, Bastian Leibe, Daniel Cremers, Aljosa Osep, Laura Leal-Taixe, Liang-Chieh Chen
• Abstract summary: We present a new benchmark: Segmenting and Tracking Every Pixel (STEP) Our work is the first that targets this task in a real-world setting that requires dense interpretation in both spatial and temporal domains. For measuring the performance, we propose a novel evaluation metric and Tracking Quality (STQ)
• Score: 107.23184053133636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we tackle video panoptic segmentation, a task that requires assigning semantic classes and track identities to all pixels in a video. To study this important problem in a setting that requires a continuous interpretation of sensory data, we present a new benchmark: Segmenting and Tracking Every Pixel (STEP), encompassing two datasets, KITTI-STEP, and MOTChallenge-STEP together with a new evaluation metric. Our work is the first that targets this task in a real-world setting that requires dense interpretation in both spatial and temporal domains. As the ground-truth for this task is difficult and expensive to obtain, existing datasets are either constructed synthetically or only sparsely annotated within short video clips. By contrast, our datasets contain long video sequences, providing challenging examples and a test-bed for studying long-term pixel-precise segmentation and tracking. For measuring the performance, we propose a novel evaluation metric Segmentation and Tracking Quality (STQ) that fairly balances semantic and tracking aspects of this task and is suitable for evaluating sequences of arbitrary length. We will make our datasets, metric, and baselines publicly available.

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