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29767 lines
1022 KiB
29767 lines
1022 KiB
<?xml version="1.0"?> |
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<!-- |
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22x18 upperbody detector (see the detailed description below). |
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////////////////////////////////////////////////////////////////////////// |
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| Contributors License Agreement |
|
| 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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| |
|
| 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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| |
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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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| |
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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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| 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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| Top |
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////////////////////////////////////////////////////////////////////////// |
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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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|
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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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|
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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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|
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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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|
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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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|
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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.cz |
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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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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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|
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2) Don't 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 occurring 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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|
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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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