Video Panoptic Segmentation

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Panoptic segmentation has become a new standard of visual recognition task by unifying previous semantic segmentation and instance segmentation tasks in concert. In this paper, we propose and explore a new video extension of this task, called video panoptic segmentation. The task requires generating consistent panoptic segmentation as well as an association of instance ids across video frames. To invigorate research on this new task, we present two types of video panoptic datasets. The first is a re-organization of the synthetic VIPER dataset into the video panoptic format to exploit its large-scale pixel annotations. The second is a temporal extension on the Cityscapes val. set, by providing new video panoptic annotations (Cityscapes-VPS). Moreover, we propose a novel video panoptic segmentation network (VPSNet) which jointly predicts object classes, bounding boxes, masks, instance id tracking, and semantic segmentation in video frames. To provide appropriate metrics for this task, we propose a video panoptic quality (VPQ) metric and evaluate our method and several other baselines. Experimental results demonstrate the effectiveness of the presented two datasets. We achieve state-of-the-art results in image PQ on Cityscapes and also in VPQ on Cityscapes-VPS and VIPER datasets.
Publisher
IEEE Conference on Computer Vision and Pattern Recognition
Issue Date
2020-06
Language
English
Citation

IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, pp.9856 - 9865

ISSN
1063-6919
DOI
10.1109/CVPR42600.2020.00988
URI
http://hdl.handle.net/10203/278680
Appears in Collection
EE-Conference Papers(학술회의논문)
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