Abstract
Detecting and estimating the presence and pose of a person in an image is a challenging problem. Literature has dealt with this as two separate problems. In this paper, we propose a system that introduces novel steps to segment the foreground object from the back ground and classifies the pose of the detected human as frontal, profile or back view. We use this as a front end to an intelligent environment we are developing to assist individuals who are blind in office spaces. The traditional background subtraction often results in silhouettes that are discontinuous, containing holes. We have incorporated the graph cut algorithm on top of background subtraction result and have observed a significant improvement in the performance of segmentation yielding continuous silhouettes without any holes. We then extract shape context features from the silhouette for training a classifier to distinguish between profile and nonprofile(frontal or back) views. Our system has shown promising results by achieving an accuracy of 87.5% for classifying profile and non profile views using an SVM on the real data sets that we have collected for our experiments.
| Original language | English (US) |
|---|---|
| Title of host publication | VISAPP 2007 - Proceedings of the 2nd International Conference on Computer Vision Theory and Applications |
| Pages | 137-142 |
| Number of pages | 6 |
| Volume | IU |
| Edition | MTSV/- |
| State | Published - 2007 |
| Event | 2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 - Barcelona, Spain Duration: Mar 8 2007 → Mar 11 2007 |
Other
| Other | 2nd International Conference on Computer Vision Theory and Applications, VISAPP 2007 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 3/8/07 → 3/11/07 |
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Keywords
- Graph cut
- Human detection
- Shape context
- SVM
ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Software
Cite this
Detecting and classifying frontal, back and profile views of humans. / Krishnan, Narayanan Chatapuram; Li, Baoxin; Panchanathan, Sethuraman.
VISAPP 2007 - Proceedings of the 2nd International Conference on Computer Vision Theory and Applications. Vol. IU MTSV/-. ed. 2007. p. 137-142.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detecting and classifying frontal, back and profile views of humans
AU - Krishnan, Narayanan Chatapuram
AU - Li, Baoxin
AU - Panchanathan, Sethuraman
PY - 2007
Y1 - 2007
N2 - Detecting and estimating the presence and pose of a person in an image is a challenging problem. Literature has dealt with this as two separate problems. In this paper, we propose a system that introduces novel steps to segment the foreground object from the back ground and classifies the pose of the detected human as frontal, profile or back view. We use this as a front end to an intelligent environment we are developing to assist individuals who are blind in office spaces. The traditional background subtraction often results in silhouettes that are discontinuous, containing holes. We have incorporated the graph cut algorithm on top of background subtraction result and have observed a significant improvement in the performance of segmentation yielding continuous silhouettes without any holes. We then extract shape context features from the silhouette for training a classifier to distinguish between profile and nonprofile(frontal or back) views. Our system has shown promising results by achieving an accuracy of 87.5% for classifying profile and non profile views using an SVM on the real data sets that we have collected for our experiments.
AB - Detecting and estimating the presence and pose of a person in an image is a challenging problem. Literature has dealt with this as two separate problems. In this paper, we propose a system that introduces novel steps to segment the foreground object from the back ground and classifies the pose of the detected human as frontal, profile or back view. We use this as a front end to an intelligent environment we are developing to assist individuals who are blind in office spaces. The traditional background subtraction often results in silhouettes that are discontinuous, containing holes. We have incorporated the graph cut algorithm on top of background subtraction result and have observed a significant improvement in the performance of segmentation yielding continuous silhouettes without any holes. We then extract shape context features from the silhouette for training a classifier to distinguish between profile and nonprofile(frontal or back) views. Our system has shown promising results by achieving an accuracy of 87.5% for classifying profile and non profile views using an SVM on the real data sets that we have collected for our experiments.
KW - Graph cut
KW - Human detection
KW - Shape context
KW - SVM
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M3 - Conference contribution
AN - SCOPUS:67650233739
VL - IU
SP - 137
EP - 142
BT - VISAPP 2007 - Proceedings of the 2nd International Conference on Computer Vision Theory and Applications
ER -