Panoptic Segmentation: A Review
- URL: http://arxiv.org/abs/2111.10250v1
- Date: Fri, 19 Nov 2021 14:40:24 GMT
- Title: Panoptic Segmentation: A Review
- Authors: Omar Elharrouss, Somaya Al-Maadeed, Nandhini Subramanian, Najmath
Ottakath, Noor Almaadeed, and Yassine Himeur
- Abstract summary: This paper presents the first comprehensive review of existing panoptic segmentation methods.
Panoptic segmentation is currently under study to help gain a more nuanced knowledge of the image scenes for video surveillance, crowd counting, self-autonomous driving, medical image analysis.
- Score: 2.270719568619559
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Image segmentation for video analysis plays an essential role in different
research fields such as smart city, healthcare, computer vision and geoscience,
and remote sensing applications. In this regard, a significant effort has been
devoted recently to developing novel segmentation strategies; one of the latest
outstanding achievements is panoptic segmentation. The latter has resulted from
the fusion of semantic and instance segmentation. Explicitly, panoptic
segmentation is currently under study to help gain a more nuanced knowledge of
the image scenes for video surveillance, crowd counting, self-autonomous
driving, medical image analysis, and a deeper understanding of the scenes in
general. To that end, we present in this paper the first comprehensive review
of existing panoptic segmentation methods to the best of the authors'
knowledge. Accordingly, a well-defined taxonomy of existing panoptic techniques
is performed based on the nature of the adopted algorithms, application
scenarios, and primary objectives. Moreover, the use of panoptic segmentation
for annotating new datasets by pseudo-labeling is discussed. Moving on,
ablation studies are carried out to understand the panoptic methods from
different perspectives. Moreover, evaluation metrics suitable for panoptic
segmentation are discussed, and a comparison of the performance of existing
solutions is provided to inform the state-of-the-art and identify their
limitations and strengths. Lastly, the current challenges the subject
technology faces and the future trends attracting considerable interest in the
near future are elaborated, which can be a starting point for the upcoming
research studies. The papers provided with code are available at:
https://github.com/elharroussomar/Awesome-Panoptic-Segmentation
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