Abstract
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 language | English |
|---|---|
| Pages (from-to) | 28409-28421 |
| Number of pages | 13 |
| Journal | Multimedia Tools and Applications |
| Volume | 78 |
| Issue number | 20 |
| DOIs | |
| Publication status | Published - 2019 Oct 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Media Technology
- Hardware and Architecture
- Computer Networks and Communications
Cite this
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Real-time human segmentation from RGB-D video sequence based on adaptive geodesic distance computation. / Kim, Yeong Seok; Yoon, Jong Chul; Lee, In Kwon.
In: Multimedia Tools and Applications, Vol. 78, No. 20, 01.10.2019, p. 28409-28421.Research output: Contribution to journal › Article
TY - JOUR
T1 - Real-time human segmentation from RGB-D video sequence based on adaptive geodesic distance computation
AU - Kim, Yeong Seok
AU - Yoon, Jong Chul
AU - Lee, In Kwon
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85033385657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85033385657&partnerID=8YFLogxK
U2 - 10.1007/s11042-017-5375-5
DO - 10.1007/s11042-017-5375-5
M3 - Article
AN - SCOPUS:85033385657
VL - 78
SP - 28409
EP - 28421
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
SN - 1380-7501
IS - 20
ER -