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@TECHREPORT{Yao_Idiap-RR-19-2009,
                      author = {Yao, Jian and Odobez, Jean-Marc},
                    projects = {Idiap},
                       month = {7},
                       title = {Fast Human Detection in Videos using Joint Appearance and Foreground Learning from Covariances of Image Feature Subsets},
                        type = {Idiap-RR},
                      number = {Idiap-RR-19-2009},
                        year = {2009},
                 institution = {Idiap},
                    abstract = {We present a fast method to detect humans from 
stationary surveillance videos. 
Traditional approaches exploit
background subtraction as an attentive filter, 
by applying the still image detectors only on foreground regions.
This doesn't take into account   that foreground observations
contain human shape  information
which can be used for detection.
To address this issue, we propose a method that 
learn the correlation between appearance and 
foreground information. It is based on a cascade 
of LogitBoost classifiers
which uses covariance matrices computed from 
appearance and foreground  features as object descriptors.
We account for the fact that covariance 
matrices lie in a Riemanian space, introduce different 
novelties -like exploiting only covariance sub-matrices- 
to reduce the induced computation load,
as well as an image rectification scheme  to remove the slant 
of people in images  
when dealing with wide angle cameras.
Evaluation on  a large set of videos 
shows that our approach performs better
than the attentive filter paradigm 
while   processing from 5 to 20 frames/sec.
In addition, on the INRIA human (static image) benchmark database,
our sub-matrix approach performs better than the full covariance
case while reducing the computation cost by more than one order
of magnitude.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2009/Yao_Idiap-RR-19-2009.pdf}
}