Performance Evaluation of the Covariance Descriptor for Target Detection

Performance Evaluation of the Covariance Descriptor for Target Detection. Cortez-Cargill, P., Undurraga-Rius, C., Mery, D., & Soto, A. In Proc. of XXVIII Int. Conf. of the Chilean Computer Science Society/IEEE CS Press, 2009.

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In computer vision, there has been a strong advance in creating new image descriptors. A descriptor that has recently appeared is the Covariance Descriptor, but there have not been any studies about the different methodologies for its construction. To address this problem we have conducted an analysis on the contribution of diverse features of an image to the descriptor and therefore their contribution to the detection of varied targets, in our case: faces and pedestrians. That is why we have defined a methodology to determinate the performance of the covariance matrix created from different characteristics. Now we are able to determinate the best set of features for face and people detection, for each problem. We have also achieved to establish that not any kind of combination of features can be used because it might not exist a correlation between them. Finally, when an analysis is performed with the best set of features, for the face detection problem we reach a performance of 99%, meanwhile for the pedestrian detection problem we reach a performance of 85%. With this we hope we have built a more solid base when choosing features for this descriptor, allowing to move forward to other topics such as object recognition or tracking.

@InProceedings{ cortez:etal:2009,
author = {P. Cortez-Cargill and C. Undurraga-Rius and D. Mery and A.
Soto},
title = {Performance Evaluation of the Covariance Descriptor for
Target Detection},
booktitle = {Proc. of XXVIII Int. Conf. of the Chilean Computer Science
Society/IEEE CS Press},
year = {2009},
abstract = {In computer vision, there has been a strong advance in
creating new image descriptors. A descriptor that has
recently appeared is the Covariance Descriptor, but there
have not been any studies about the different methodologies
for its construction. To address this problem we have
conducted an analysis on the contribution of diverse
features of an image to the descriptor and therefore their
contribution to the detection of varied targets, in our
case: faces and pedestrians. That is why we have defined a
methodology to determinate the performance of the
covariance matrix created from different characteristics.
Now we are able to determinate the best set of features for
face and people detection, for each problem. We have also
achieved to establish that not any kind of combination of
features can be used because it might not exist a
correlation between them. Finally, when an analysis is
performed with the best set of features, for the face
detection problem we reach a performance of 99%, meanwhile
for the pedestrian detection problem we reach a performance
of 85%. With this we hope we have built a more solid base
when choosing features for this descriptor, allowing to
move forward to other topics such as object recognition or
tracking. },
url = {http://saturno.ing.puc.cl/media/papers_alvaro/Final-Proceedings-Cortez_Undurraga_Mery_Soto_SCCC2009.pdf}
}

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