haarcascade_fullbody.rar_Fullbody detection_haarcascade_fullbody

<?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 --> <_> <!-- root node --> <feature> <rects> <_>1 4 12 23 -1.</_> <_>5 4 4 23 3.</_></rects> <tilted>0</tilted></feature> <threshold>-0.2674087882041931</threshold> <left_val>0.8308296203613281</left_val> <right_val>-0.2259958982467651</right_val></_></_> <_> <!-- tree 3 --> <_> <!-- root node --> <feature> <rects> <_>1 7 12 9 -1.</_> <_>4 7 6 9 2.</_></rects> <tilted>0</tilted></feature> <threshold>0.0964197367429733</threshold> <left_val>-0.1169784963130951<