From 3d1048d79ad09d156836ff22edb9909d038fce87 Mon Sep 17 00:00:00 2001
From: Yves Fischer <yvesf-git@xapek.org>
Date: Sat, 21 Mar 2015 23:32:10 +0100
Subject: notifications

---
 cv-head-lock/xml/haarcascade_fullbody.xml | 18118 ++++++++++++++++++++++++++++
 1 file changed, 18118 insertions(+)
 create mode 100644 cv-head-lock/xml/haarcascade_fullbody.xml

(limited to 'cv-head-lock/xml/haarcascade_fullbody.xml')

diff --git a/cv-head-lock/xml/haarcascade_fullbody.xml b/cv-head-lock/xml/haarcascade_fullbody.xml
new file mode 100644
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--- /dev/null
+++ b/cv-head-lock/xml/haarcascade_fullbody.xml
@@ -0,0 +1,18118 @@
+<?xml version="1.0"?>
+<!--
+   14x28 fullbody 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_fullbody type_id="opencv-haar-classifier">
+  <size>14 28</size>
+  <stages>
+    <_>
+      <!-- stage 0 -->
+      <trees>
+        <_>
+          <!-- tree 0 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>1 5 12 21 -1.</_>
+                <_>5 5 4 21 3.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-0.0558205693960190</threshold>
+            <left_val>0.5869792103767395</left_val>
+            <right_val>-0.6281142234802246</right_val></_></_>
+        <_>
+          <!-- tree 1 -->
+          <_>
+            <!-- root node -->
+            <feature>
+              <rects>
+                <_>9 2 3 26 -1.</_>
+                <_>9 15 3 13 2.</_></rects>
+              <tilted>0</tilted></feature>
+            <threshold>-0.0388611815869808</threshold>
+            <left_val>-0.7091681957244873</left_val>
+            <right_val>0.2682121098041534</right_val></_></_>
+        <_>
+          <!-- tree 2 -->
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+            <feature>
+              <rects>
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+              <tilted>0</tilted></feature>
+            <threshold>-0.2674087882041931</threshold>
+            <left_val>0.8308296203613281</left_val>
+            <right_val>-0.2259958982467651</right_val></_></_>
+        <_>
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+            <threshold>0.0964197367429733</threshold>
+            <left_val>-0.1169784963130951</left_val>
+            <right_val>0.8725455999374390</right_val></_></_>
+        <_>
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+            <threshold>-0.0107987103983760</threshold>
+            <left_val>-0.5721974968910217</left_val>
+            <right_val>0.2532565891742706</right_val></_></_>
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+            <left_val>0.1965083032846451</left_val>
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+      <stage_threshold>-1.2288980484008789</stage_threshold>
+      <parent>-1</parent>
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-- 
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