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@ARTICLE{Yao_CVIU_2011,
         author = {Yao, Jian and Odobez, Jean-Marc},
       keywords = {covariance matrices , human detection, image rectification , information fusion , learning, real-time, surveillance},
       projects = {Idiap},
          month = oct,
          title = {Fast Human Detection from  Joint Appearance and Foreground Feature Subset Covariances},
        journal = {Computer Vision and Image Understanding},
         volume = {115},
         number = {10},
           year = {2011},
          pages = {1414-1426},
       abstract = {We present a fast method to detect humans from stationary surveillance videos. It is based on a cascade of LogitBoost classifiers which use  covariance matrices as object descriptors. We have made several contributions. First, our method learns the correlation between appearance and foreground features and show that the human shape information contained in foreground observations can dramatically improve performance when used jointly with appearance cues. This contrasts with traditional approaches that exploit background subtraction as an attentive filter, by applying still image detectors only on foreground regions.  As a second contribution, we show that using  the covariance matrices of feature subsets rather than of the full set in boosting provides similar or better performance while significantly reducing the  computation load. The last  contribution is a simple image rectification scheme that  removes the slant of people in images when dealing with wide angle cameras,
allowing for the appropriate  use of integral images. Extensive experiments on  a large video set show that our approach performs much better than the attentive filter paradigm while   processing 5 to 20 frames/sec. The efficiency  of our subset approach with state-of-the-art results is also demonstrated on the INRIA human (static image) database.},
            pdf = {https://publications.idiap.ch/attachments/papers/2011/Yao_CVIU_2011.pdf}
}