Abstract
In recent years, local pattern based features have attracted increasing interest in object detection and recognition systems. Local Binary Pattern (LBP) feature is widely used in texture classification and face detection. But the original definition of LBP is not suitable for human detection. In this paper, we propose a novel feature set named gradient local binary patterns (GLBP), Original GLBP and Improved GLBP, for human detection. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is more discriminative than histogram of orientated gradient (HOG), histogram of template (HOT) and Semantic Local Binary Patterns (S-LBP), under the same training method. In our experiments, the window size is fixed. That means the performance can be improved by boosting and cascade methods. And the computation of GLBP feature is parallel, which make it easy for hardware acceleration. These factors make GLBP feature possible for real-time human detection.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - IEEE International Symposium on Circuits and Systems |
| Pages | 978-981 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2013 |
| Event | 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 - Beijing Duration: 2013 May 19 → 2013 May 23 |
Other
| Other | 2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 |
|---|---|
| City | Beijing |
| Period | 13/5/19 → 13/5/23 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
Cite this
Gradient Local Binary Patterns for human detection. / Jiang, Ning; Xu, Jiu; Yu, Wenxin; Goto, Satoshi.
Proceedings - IEEE International Symposium on Circuits and Systems. 2013. p. 978-981 6572012.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Gradient Local Binary Patterns for human detection
AU - Jiang, Ning
AU - Xu, Jiu
AU - Yu, Wenxin
AU - Goto, Satoshi
PY - 2013
Y1 - 2013
N2 - In recent years, local pattern based features have attracted increasing interest in object detection and recognition systems. Local Binary Pattern (LBP) feature is widely used in texture classification and face detection. But the original definition of LBP is not suitable for human detection. In this paper, we propose a novel feature set named gradient local binary patterns (GLBP), Original GLBP and Improved GLBP, for human detection. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is more discriminative than histogram of orientated gradient (HOG), histogram of template (HOT) and Semantic Local Binary Patterns (S-LBP), under the same training method. In our experiments, the window size is fixed. That means the performance can be improved by boosting and cascade methods. And the computation of GLBP feature is parallel, which make it easy for hardware acceleration. These factors make GLBP feature possible for real-time human detection.
AB - In recent years, local pattern based features have attracted increasing interest in object detection and recognition systems. Local Binary Pattern (LBP) feature is widely used in texture classification and face detection. But the original definition of LBP is not suitable for human detection. In this paper, we propose a novel feature set named gradient local binary patterns (GLBP), Original GLBP and Improved GLBP, for human detection. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is more discriminative than histogram of orientated gradient (HOG), histogram of template (HOT) and Semantic Local Binary Patterns (S-LBP), under the same training method. In our experiments, the window size is fixed. That means the performance can be improved by boosting and cascade methods. And the computation of GLBP feature is parallel, which make it easy for hardware acceleration. These factors make GLBP feature possible for real-time human detection.
UR - http://www.scopus.com/inward/record.url?scp=84883314620&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883314620&partnerID=8YFLogxK
U2 - 10.1109/ISCAS.2013.6572012
DO - 10.1109/ISCAS.2013.6572012
M3 - Conference contribution
AN - SCOPUS:84883314620
SN - 9781467357609
SP - 978
EP - 981
BT - Proceedings - IEEE International Symposium on Circuits and Systems
ER -