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+<?xml version="1.0"?>
+<!--
+ 22x18 upperbody detector (see the detailed description below).
+
+//////////////////////////////////////////////////////////////////////////
+| Contributors License Agreement
+| IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+| By downloading, copying, installing or using the software you agree
+| to this license.
+| If you do not agree to this license, do not download, install,
+| copy or use the software.
+|
+| Copyright (c) 2004, Hannes Kruppa and Bernt Schiele (ETH Zurich, Switzerland).
+| All rights reserved.
+|
+| Redistribution and use in source and binary forms, with or without
+| modification, are permitted provided that the following conditions are
+| met:
+|
+| * Redistributions of source code must retain the above copyright
+| notice, this list of conditions and the following disclaimer.
+| * Redistributions in binary form must reproduce the above
+| copyright notice, this list of conditions and the following
+| disclaimer in the documentation and/or other materials provided
+| with the distribution.
+| * The name of Contributor may not used to endorse or promote products
+| derived from this software without specific prior written permission.
+|
+| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+| "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+| LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+| A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+| CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+| EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+| PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+| PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+| LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+| NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+| SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Back to
+| Top
+//////////////////////////////////////////////////////////////////////////
+
+"Haar"-based Detectors For Pedestrian Detection
+===============================================
+by Hannes Kruppa and Bernt Schiele, ETH Zurich, Switzerland
+
+This archive provides the following three detectors:
+- upper body detector (most fun, useful in many scenarios!)
+- lower body detector
+- full body detector
+
+These detectors have been successfully applied to pedestrian detection
+in still images. They can be directly passed as parameters to the
+program HaarFaceDetect.
+NOTE: These detectors deal with frontal and backside views but not
+with side views (also see "Known limitations" below).
+
+RESEARCHERS:
+If you are using any of the detectors or involved ideas please cite
+this paper (available at www.vision.ethz.ch/publications/):
+
+@InProceedings{Kruppa03-bmvc,
+ author = "Hannes Kruppa, Modesto Castrillon-Santana and Bernt Schiele",
+ title = "Fast and Robust Face Finding via Local Context."
+ booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
+ year = "2003",
+ month = "October"
+}
+
+COMMERCIAL:
+If you have any commercial interest in this work please contact
+hkruppa@inf.ethz.ch
+
+
+ADDITIONAL INFORMATION
+======================
+Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player
+that can play back .mpg movies.
+Under Linux that's:
+> ffplay demo.mpg
+or:
+> mplayer demo.mpg
+
+The movie shows a person walking towards the camera in a realistic
+indoor setting. Using ffplay or mplayer you can pause and continue the
+movie by pressing the space bar.
+
+Detections coming from the different detectors are visualized using
+different line styles:
+upper body : dotted line
+lower body : dashed line
+full body : solid line
+
+You will notice that successful detections containing the target do
+not sit tightly on the body but also include some of the background
+left and right. This is not a bug but accurately reflects the
+employed training data which also includes portions of the background
+to ensure proper silhouette representation. If you want to get a
+feeling for the training data check out the CBCL data set:
+http://www.ai.mit.edu/projects/cbcl/software-datasets/PedestrianData.html
+
+There is also a small number of false alarms in this sequence.
+NOTE: This is per frame detection, not tracking (which is also one of
+the reasons why it is not mislead by the person's shadow on the back
+wall).
+
+On an Intel Xeon 1.7GHz machine the detectors operate at something
+between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the
+detector. The detectors work as well on much lower image resolutions
+which is always an interesting possibility for speed-ups or
+"coarse-to-fine" search strategies.
+
+Additional information e.g. on training parameters, detector
+combination, detecting other types of objects (e.g. cars) etc. is
+available in my PhD thesis report (available end of June). Check out
+www.vision.ethz.ch/kruppa/
+
+
+KNOWN LIMITATIONS
+==================
+1) the detectors only support frontal and back views but not sideviews.
+ Sideviews are trickier and it makes a lot of sense to include additional
+ modalities for their detection, e.g. motion information. I recommend
+ Viola and Jones' ICCV 2003 paper if this further interests you.
+
+2) dont expect these detectors to be as accurate as a frontal face detector.
+ A frontal face as a pattern is pretty distinct with respect to other
+ patterns occuring in the world (i.e. image "background"). This is not so
+ for upper, lower and especially full bodies, because they have to rely
+ on fragile silhouette information rather than internal (facial) features.
+ Still, we found especially the upper body detector to perform amazingly well.
+ In contrast to a face detector these detectors will also work at very low
+ image resolutions
+
+Acknowledgements
+================
+Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
+-->
+<opencv_storage>
+<haarcascade_upperbody type_id="opencv-haar-classifier">
+ <size>22 18</size>
+ <stages>
+ <_>
+ <!-- stage 0 -->
+ <trees>
+ <_>
+ <!-- tree 0 -->
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+ <!-- root node -->
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+ <tilted>0</tilted></feature>
+ <threshold>-0.0136960297822952</threshold>
+ <left_val>0.4507646858692169</left_val>
+ <right_val>-0.4217903017997742</right_val></_></_>
+ <_>
+ <!-- tree 1 -->
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+ <feature>
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+ <tilted>0</tilted></feature>
+ <threshold>0.0124414497986436</threshold>
+ <left_val>0.1649325042963028</left_val>
+ <right_val>-0.7479348778724670</right_val></_></_>
+ <_>
+ <!-- tree 2 -->
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+ <!-- root node -->
+ <feature>
+ <rects>
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+ <tilted>0</tilted></feature>
+ <threshold>-2.7094660326838493e-003</threshold>
+ <left_val>0.3100470006465912</left_val>
+ <right_val>-0.3761714100837708</right_val></_></_>
+ <_>
+ <!-- tree 3 -->
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+ <tilted>1</tilted></feature>
+ <threshold>-0.1000801026821137</threshold>
+ <left_val>0.7618219852447510</left_val>
+ <right_val>-0.0745569765567780</right_val></_></_>
+ <_>
+ <!-- tree 4 -->
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+ <threshold>-0.2511411905288696</threshold>
+ <left_val>-0.6415402889251709</left_val>
+ <right_val>0.1513922065496445</right_val></_></_>
+ <_>
+ <!-- tree 5 -->
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+ <threshold>-0.1051065027713776</threshold>
+ <left_val>0.7145937085151672</left_val>
+ <right_val>-0.1449857950210571</right_val></_></_>
+ <_>
+ <!-- tree 6 -->
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+ <threshold>-0.0884480178356171</threshold>
+ <left_val>0.7577317953109741</left_val>
+ <right_val>-0.0685868933796883</right_val></_></_>
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+ <!-- tree 7 -->
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+ <left_val>0.1461060941219330</left_val>
+ <right_val>-0.5426371097564697</right_val></_></_>
+ <_>
+ <!-- tree 8 -->
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+ <threshold>0.0126905702054501</threshold>
+ <left_val>0.1167458966374397</left_val>
+ <right_val>-0.4964945912361145</right_val></_></_>
+ <_>
+ <!-- tree 9 -->
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+ <left_val>-0.3852939009666443</left_val>
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+ <left_val>0.2401147037744522</left_val>
+ <right_val>-0.2602663934230804</right_val></_></_></trees>
+ <stage_threshold>-1.1264339685440063</stage_threshold>
+ <parent>-1</parent>
+ <next>-1</next></_>
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