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14057 lines
386 KiB
XML
14057 lines
386 KiB
XML
<?xml version="1.0"?>
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<!--
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19x23 lowerbody detector (see the detailed description below).
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//////////////////////////////////////////////////////////////////////////
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| Contributors License Agreement
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| IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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| By downloading, copying, installing or using the software you agree
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| to this license.
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| If you do not agree to this license, do not download, install,
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| copy or use the software.
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|
|
|
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| Copyright (c) 2004, Hannes Kruppa and Bernt Schiele (ETH Zurich, Switzerland).
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| All rights reserved.
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|
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| Redistribution and use in source and binary forms, with or without
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| modification, are permitted provided that the following conditions are
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| met:
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| * Redistributions of source code must retain the above copyright
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| notice, this list of conditions and the following disclaimer.
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| * Redistributions in binary form must reproduce the above
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| copyright notice, this list of conditions and the following
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| disclaimer in the documentation and/or other materials provided
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| with the distribution.
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| * The name of Contributor may not used to endorse or promote products
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| derived from this software without specific prior written permission.
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| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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| "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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| LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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| A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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| CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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| EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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| PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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| PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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| LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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| NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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| SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Back to
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//////////////////////////////////////////////////////////////////////////
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"Haar"-based Detectors For Pedestrian Detection
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===============================================
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by Hannes Kruppa and Bernt Schiele, ETH Zurich, Switzerland
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This archive provides the following three detectors:
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- upper body detector (most fun, useful in many scenarios!)
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- lower body detector
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- full body detector
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These detectors have been successfully applied to pedestrian detection
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in still images. They can be directly passed as parameters to the
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program HaarFaceDetect.
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NOTE: These detectors deal with frontal and backside views but not
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with side views (also see "Known limitations" below).
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RESEARCHERS:
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If you are using any of the detectors or involved ideas please cite
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this paper (available at www.vision.ethz.ch/publications/):
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@InProceedings{Kruppa03-bmvc,
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author = "Hannes Kruppa, Modesto Castrillon-Santana and Bernt Schiele",
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title = "Fast and Robust Face Finding via Local Context."
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booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
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year = "2003",
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month = "October"
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}
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COMMERCIAL:
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If you have any commercial interest in this work please contact
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hkruppa@inf.ethz.ch
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ADDITIONAL INFORMATION
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======================
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Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player
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that can play back .mpg movies.
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Under Linux that's:
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> ffplay demo.mpg
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or:
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> mplayer demo.mpg
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The movie shows a person walking towards the camera in a realistic
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indoor setting. Using ffplay or mplayer you can pause and continue the
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movie by pressing the space bar.
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Detections coming from the different detectors are visualized using
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different line styles:
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upper body : dotted line
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lower body : dashed line
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full body : solid line
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You will notice that successful detections containing the target do
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not sit tightly on the body but also include some of the background
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left and right. This is not a bug but accurately reflects the
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employed training data which also includes portions of the background
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to ensure proper silhouette representation. If you want to get a
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feeling for the training data check out the CBCL data set:
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http://www.ai.mit.edu/projects/cbcl/software-datasets/PedestrianData.html
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There is also a small number of false alarms in this sequence.
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NOTE: This is per frame detection, not tracking (which is also one of
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the reasons why it is not mislead by the person's shadow on the back
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wall).
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On an Intel Xeon 1.7GHz machine the detectors operate at something
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between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the
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detector. The detectors work as well on much lower image resolutions
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which is always an interesting possibility for speed-ups or
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"coarse-to-fine" search strategies.
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Additional information e.g. on training parameters, detector
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combination, detecting other types of objects (e.g. cars) etc. is
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available in my PhD thesis report (available end of June). Check out
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www.vision.ethz.ch/kruppa/
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|
KNOWN LIMITATIONS
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==================
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1) the detectors only support frontal and back views but not sideviews.
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Sideviews are trickier and it makes a lot of sense to include additional
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modalities for their detection, e.g. motion information. I recommend
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Viola and Jones' ICCV 2003 paper if this further interests you.
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2) dont expect these detectors to be as accurate as a frontal face detector.
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A frontal face as a pattern is pretty distinct with respect to other
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patterns occuring in the world (i.e. image "background"). This is not so
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for upper, lower and especially full bodies, because they have to rely
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on fragile silhouette information rather than internal (facial) features.
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Still, we found especially the upper body detector to perform amazingly well.
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In contrast to a face detector these detectors will also work at very low
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image resolutions
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Acknowledgements
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================
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Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
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-->
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