Abstract
| Language | English |
|---|---|
| Title of host publication | Advances in Multimedia Information Processing - PCM 2016 |
| Editors | Enqing Chen, Yihong Gong, Yun Tie |
| Place of Publication | Cham, Switzerland |
| Publisher | Springer |
| Pages | 697-704 |
| Number of pages | 8 |
| ISBN (Print) | 9783319488950 |
| DOIs | |
| Publication status | E-pub ahead of print - 27 Nov 2016 |
| Event | 17th Pacific-Rim Conference on Multimedia (PCM 2016) - Xi'an, China Duration: 15 Sep 2016 → 15 Sep 2016 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 9917 |
| ISSN (Print) | 0302-9743 |
Conference
| Conference | 17th Pacific-Rim Conference on Multimedia (PCM 2016) |
|---|---|
| Country | China |
| City | Xi'an |
| Period | 15/09/16 → 15/09/16 |
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Keywords
- video salient objects
- pedestrian detection/tracking
- image fusion
- visible image
- thermal image
Cite this
}
Fusion of thermal and visible imagery for effective detection and tracking of salient objects in videos. / Yan, Yijun; Ren, Jinchang; Zhao, Huimin; Zheng, Jiangbin; Zaihidee, Ezrinda Mohd; Soraghan, John.
Advances in Multimedia Information Processing - PCM 2016. ed. / Enqing Chen; Yihong Gong; Yun Tie. Cham, Switzerland : Springer, 2016. p. 697-704 (Lecture Notes in Computer Science; Vol. 9917).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Fusion of thermal and visible imagery for effective detection and tracking of salient objects in videos
AU - Yan, Yijun
AU - Ren, Jinchang
AU - Zhao, Huimin
AU - Zheng, Jiangbin
AU - Zaihidee, Ezrinda Mohd
AU - Soraghan, John
PY - 2016/11/27
Y1 - 2016/11/27
N2 - In this paper, we present an efficient approach to detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by background-subtraction for foreground detection in visible images. Meanwhile, adaptively thresholding is applied for foreground detection in thermal domain as human objects tend to be of higher temperature thus brighter than the background. To deal with cases of occlusion, prediction based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. The proposed method is evaluated on OTCBVS, a publicly available color-thermal benchmark dataset. Promising results have shown that the proposed fusion based approach can successfully detect and track multiple human objects.
AB - In this paper, we present an efficient approach to detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by background-subtraction for foreground detection in visible images. Meanwhile, adaptively thresholding is applied for foreground detection in thermal domain as human objects tend to be of higher temperature thus brighter than the background. To deal with cases of occlusion, prediction based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. The proposed method is evaluated on OTCBVS, a publicly available color-thermal benchmark dataset. Promising results have shown that the proposed fusion based approach can successfully detect and track multiple human objects.
KW - video salient objects
KW - pedestrian detection/tracking
KW - image fusion
KW - visible image
KW - thermal image
UR - https://link.springer.com/book/10.1007/978-3-319-48890-5
U2 - 10.1007/978-3-319-48896-7_69
DO - 10.1007/978-3-319-48896-7_69
M3 - Conference contribution book
SN - 9783319488950
T3 - Lecture Notes in Computer Science
SP - 697
EP - 704
BT - Advances in Multimedia Information Processing - PCM 2016
A2 - Chen, Enqing
A2 - Gong, Yihong
A2 - Tie, Yun
PB - Springer
CY - Cham, Switzerland
ER -