Real-time human segmentation from RGB-D video sequence based on adaptive geodesic distance computation

Yeong Seok Kim, Jong Chul Yoon, In Kwon Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


In this paper, we propose a method for extracting humans in the foreground of video frames using color and depth information. To ensure real-time performance and to increase accuracy, we classify a video frame into two parts by degree of noise: head region with high noise level, and non-head region with low noise level. Then, we apply a high-computational geodesic matting algorithm to the noisy head region that includes hair, and a low-computational hole filling with smoothing method to other regions. Additionally, we modify the traditional color-based geodesic segmentation algorithm to consider additional depth information. Then, we apply temporal/spatial smoothing to the blended foreground mask in order to enhance the coherence between video frames. Experimental results show that the proposed method outperforms a previous approach by accuracy and performance.

Original languageEnglish
Pages (from-to)28409-28421
Number of pages13
JournalMultimedia Tools and Applications
Issue number20
Publication statusPublished - 2019 Oct 1

Bibliographical note

Funding Information:
This work was supported by ZoYool Co., Ltd. and KCTVJEJU Co., Ltd.

Publisher Copyright:
© 2017, Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications


Dive into the research topics of 'Real-time human segmentation from RGB-D video sequence based on adaptive geodesic distance computation'. Together they form a unique fingerprint.

Cite this