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Diffstat (limited to 'cv-head-lock/xml/haarcascade_upperbody.xml')
| -rw-r--r-- | cv-head-lock/xml/haarcascade_upperbody.xml | 29767 |
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diff --git a/cv-head-lock/xml/haarcascade_upperbody.xml b/cv-head-lock/xml/haarcascade_upperbody.xml new file mode 100644 index 0000000..45eeac9 --- /dev/null +++ b/cv-head-lock/xml/haarcascade_upperbody.xml @@ -0,0 +1,29767 @@ +<?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 --> + <_> + <!-- root node --> + <feature> + <rects> + <_>5 5 12 6 -1.</_> + <_>9 5 4 6 3.</_></rects> + <tilted>0</tilted></feature> + <threshold>-0.0136960297822952</threshold> + <left_val>0.4507646858692169</left_val> + <right_val>-0.4217903017997742</right_val></_></_> + <_> + <!-- tree 1 --> + <_> + <!-- root node --> + <feature> + <rects> + <_>7 13 10 4 -1.</_> + <_>7 15 10 2 2.</_></rects> + <tilted>0</tilted></feature> + <threshold>0.0124414497986436</threshold> + <left_val>0.1649325042963028</left_val> + <right_val>-0.7479348778724670</right_val></_></_> + <_> + <!-- tree 2 --> + <_> + <!-- root node --> + <feature> + <rects> + <_>3 14 9 4 -1.</_> + <_>6 14 3 4 3.</_></rects> + <tilted>0</tilted></feature> + <threshold>-2.7094660326838493e-003</threshold> + <left_val>0.3100470006465912</left_val> + <right_val>-0.3761714100837708</right_val></_></_> + <_> + <!-- tree 3 --> + <_> + <!-- root node --> + <feature> + <rects> + <_>15 6 5 6 -1.</_> + <_>15 6 5 3 2.</_></rects> + <tilted>1</tilted></feature> + <threshold>-0.1000801026821137</threshold> + <left_val>0.7618219852447510</left_val> + <right_val>-0.0745569765567780</right_val></_></_> + <_> + <!-- tree 4 --> + <_> + <!-- root node --> + <feature> + <rects> + <_>0 1 22 14 -1.</_> + <_>11 1 11 14 2.</_></rects> + <tilted>0</tilted></feature> + <threshold>-0.2511411905288696</threshold> + 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