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      1 <?xml version="1.0"?>
      2 <!--
      3    19x23 lowerbody detector (see the detailed description below).
      4 
      5 //////////////////////////////////////////////////////////////////////////
      6 | Contributors License Agreement
      7 | IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
      8 |   By downloading, copying, installing or using the software you agree
      9 |   to this license.
     10 |   If you do not agree to this license, do not download, install,
     11 |   copy or use the software.
     12 |
     13 | Copyright (c) 2004, Hannes Kruppa and Bernt Schiele (ETH Zurich, Switzerland).
     14 |  All rights reserved.
     15 |
     16 | Redistribution and use in source and binary forms, with or without
     17 | modification, are permitted provided that the following conditions are
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     41 //////////////////////////////////////////////////////////////////////////
     42 
     43 "Haar"-based Detectors For Pedestrian Detection
     44 ===============================================
     45 by Hannes Kruppa and Bernt Schiele, ETH Zurich, Switzerland
     46 
     47 This archive provides the following three detectors:
     48 - upper body detector (most fun, useful in many scenarios!)
     49 - lower body detector
     50 - full body detector
     51 
     52 These detectors have been successfully applied to pedestrian detection
     53 in still images. They can be directly passed as parameters to the
     54 program HaarFaceDetect.
     55 NOTE: These detectors deal with frontal and backside views but not
     56 with side views (also see "Known limitations" below).
     57 
     58 RESEARCHERS:
     59 If you are using any of the detectors or involved ideas please cite
     60 this paper (available at www.vision.ethz.ch/publications/):
     61 
     62 @InProceedings{Kruppa03-bmvc,
     63   author =       "Hannes Kruppa, Modesto Castrillon-Santana and Bernt Schiele",
     64   title =        "Fast and Robust Face Finding via Local Context."
     65   booktitle =    "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
     66   year =         "2003",
     67   month =        "October"
     68 }
     69 
     70 COMMERCIAL:
     71 If you have any commercial interest in this work please contact
     72 hkruppa (a] inf.ethz.ch
     73 
     74 
     75 ADDITIONAL INFORMATION
     76 ======================
     77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player
     78 that can play back .mpg movies.
     79 Under Linux that's:
     80 > ffplay demo.mpg
     81 or:
     82 > mplayer demo.mpg
     83 
     84 The movie shows a person walking towards the camera in a realistic
     85 indoor setting. Using ffplay or mplayer you can pause and continue the
     86 movie by pressing the space bar.
     87 
     88 Detections coming from the different detectors are visualized using
     89 different line styles:
     90 upper body : dotted line
     91 lower body : dashed line
     92 full body  : solid line
     93 
     94 You will notice that successful detections containing the target do
     95 not sit tightly on the body but also include some of the background
     96 left and right.  This is not a bug but accurately reflects the
     97 employed training data which also includes portions of the background
     98 to ensure proper silhouette representation. If you want to get a
     99 feeling for the training data check out the CBCL data set:
    100 http://www.ai.mit.edu/projects/cbcl/software-datasets/PedestrianData.html
    101 
    102 There is also a small number of false alarms in this sequence.
    103 NOTE: This is per frame detection, not tracking (which is also one of
    104 the reasons why it is not mislead by the person's shadow on the back
    105 wall).
    106 
    107 On an Intel Xeon 1.7GHz machine the detectors operate at something
    108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the
    109 detector. The detectors work as well on much lower image resolutions
    110 which is always an interesting possibility for speed-ups or
    111 "coarse-to-fine" search strategies.
    112 
    113 Additional information e.g. on training parameters, detector
    114 combination, detecting other types of objects (e.g. cars) etc. is
    115 available in my PhD thesis report (available end of June). Check out
    116 www.vision.ethz.ch/kruppa/
    117 
    118 
    119 KNOWN LIMITATIONS
    120 =================
    121 1) the detectors only support frontal and back views but not sideviews.
    122    Sideviews are trickier and it makes a lot of sense to include additional
    123    modalities for their detection, e.g. motion information. I recommend
    124    Viola and Jones' ICCV 2003 paper if this further interests you.
    125 
    126 2) dont expect these detectors to be as accurate as a frontal face detector.
    127    A frontal face as a pattern is pretty distinct with respect to other
    128    patterns occuring in the world (i.e. image "background"). This is not so
    129    for upper, lower and especially full bodies, because they have to rely
    130    on fragile silhouette information rather than internal (facial) features.
    131    Still, we found especially the upper body detector to perform amazingly well.
    132    In contrast to a face detector these detectors will also work at very low
    133    image resolutions
    134 
    135 Acknowledgements
    136 ================
    137 Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
    138 -->
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   1688         <_>
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   1690           <_>
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   1700         <_>
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   1736         <_>
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   1748         <_>
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   2016           <!-- tree 32 -->
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   2139             <left_val>-0.1078016981482506</left_val>
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   2141         <_>
   2142           <!-- tree 8 -->
   2143           <_>
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   2214           <!-- tree 14 -->
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   2226           <!-- tree 15 -->
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