This article compares the performance of target detectors based on adaptive background differencing on public benchmark data. Five state of the art methods are described. The performance is evaluated using state of the art measures with respect to ground truth. The original points are the comparison to hand labelled ground truth and the evaluation on a large database. The simpler methods LOTS and SGM are more appropriate to the particular task as MGM using a more complex background model.
%0 Conference Paper
%1 citeulike:8955853
%A Hall, D.
%A Nascimento, J.
%A Ribeiro, P.
%A Andrade, E.
%A Moreno, P.
%A Pesnel, S.
%A List, T.
%A Emonet, R.
%A Fisher, R. B.
%A Victor, J. S.
%A Crowley, J. L.
%B Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop on
%D 2005
%I IEEE
%K algorithm, detection, image, processing, target, video
%P 113--120
%R 10.1109/VSPETS.2005.1570905
%T Comparison of target detection algorithms using adaptive background models
%U http://dx.doi.org/10.1109/VSPETS.2005.1570905
%X This article compares the performance of target detectors based on adaptive background differencing on public benchmark data. Five state of the art methods are described. The performance is evaluated using state of the art measures with respect to ground truth. The original points are the comparison to hand labelled ground truth and the evaluation on a large database. The simpler methods LOTS and SGM are more appropriate to the particular task as MGM using a more complex background model.
%@ 0-7803-9424-0
@inproceedings{citeulike:8955853,
abstract = {{This article compares the performance of target detectors based on adaptive background differencing on public benchmark data. Five state of the art methods are described. The performance is evaluated using state of the art measures with respect to ground truth. The original points are the comparison to hand labelled ground truth and the evaluation on a large database. The simpler methods LOTS and SGM are more appropriate to the particular task as MGM using a more complex background model.}},
added-at = {2012-03-02T03:39:18.000+0100},
author = {Hall, D. and Nascimento, J. and Ribeiro, P. and Andrade, E. and Moreno, P. and Pesnel, S. and List, T. and Emonet, R. and Fisher, R. B. and Victor, J. S. and Crowley, J. L.},
biburl = {https://www.bibsonomy.org/bibtex/2fe8b91049006084ea7103f531b721e11/baby9992006},
booktitle = {Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 2005. 2nd Joint IEEE International Workshop on},
citeulike-article-id = {8955853},
citeulike-linkout-0 = {http://dx.doi.org/10.1109/VSPETS.2005.1570905},
citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1570905},
doi = {10.1109/VSPETS.2005.1570905},
institution = {INRIA Rhone-Alpes, France},
interhash = {7bfa6f82c2674f11bb435c5679562d43},
intrahash = {fe8b91049006084ea7103f531b721e11},
isbn = {0-7803-9424-0},
keywords = {algorithm, detection, image, processing, target, video},
month = oct,
pages = {113--120},
posted-at = {2012-02-26 12:25:53},
priority = {2},
publisher = {IEEE},
timestamp = {2012-03-02T03:39:20.000+0100},
title = {{Comparison of target detection algorithms using adaptive background models}},
url = {http://dx.doi.org/10.1109/VSPETS.2005.1570905},
year = 2005
}