Path: blob/master/data/haarcascades_cuda/haarcascade_fullbody.xml
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<?xml version="1.0"?>1<!--214x28 fullbody detector (see the detailed description below).34//////////////////////////////////////////////////////////////////////////5| Contributors License Agreement6| IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.7| By downloading, copying, installing or using the software you agree8| to this license.9| If you do not agree to this license, do not download, install,10| copy or use the software.11|12| Copyright (c) 2004, Hannes Kruppa and Bernt Schiele (ETH Zurich, Switzerland).13| All rights reserved.14|15| Redistribution and use in source and binary forms, with or without16| modification, are permitted provided that the following conditions are17| met:18|19| * Redistributions of source code must retain the above copyright20| notice, this list of conditions and the following disclaimer.21| * Redistributions in binary form must reproduce the above22| copyright notice, this list of conditions and the following23| disclaimer in the documentation and/or other materials provided24| with the distribution.25| * The name of Contributor may not used to endorse or promote products26| derived from this software without specific prior written permission.27|28| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS29| "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT30| LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR31| A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE32| CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,33| EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,34| PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR35| PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF36| LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING37| NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS38| SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Back to39| Top40//////////////////////////////////////////////////////////////////////////4142"Haar"-based Detectors For Pedestrian Detection43===============================================44by Hannes Kruppa and Bernt Schiele, ETH Zurich, Switzerland4546This archive provides the following three detectors:47- upper body detector (most fun, useful in many scenarios!)48- lower body detector49- full body detector5051These detectors have been successfully applied to pedestrian detection52in still images. They can be directly passed as parameters to the53program HaarFaceDetect.54NOTE: These detectors deal with frontal and backside views but not55with side views (also see "Known limitations" below).5657RESEARCHERS:58If you are using any of the detectors or involved ideas please cite59this paper (available at www.vision.ethz.ch/publications/):6061@InProceedings{Kruppa03-bmvc,62author = "Hannes Kruppa, Modesto Castrillon-Santana and Bernt Schiele",63title = "Fast and Robust Face Finding via Local Context."64booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"65year = "2003",66month = "October"67}6869COMMERCIAL:70If you have any commercial interest in this work please contact71[email protected]727374ADDITIONAL INFORMATION75======================76Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player77that can play back .mpg movies.78Under Linux that's:79> ffplay demo.mpg80or:81> mplayer demo.mpg8283The movie shows a person walking towards the camera in a realistic84indoor setting. Using ffplay or mplayer you can pause and continue the85movie by pressing the space bar.8687Detections coming from the different detectors are visualized using88different line styles:89upper body : dotted line90lower body : dashed line91full body : solid line9293You will notice that successful detections containing the target do94not sit tightly on the body but also include some of the background95left and right. This is not a bug but accurately reflects the96employed training data which also includes portions of the background97to ensure proper silhouette representation. If you want to get a98feeling for the training data check out the CBCL data set:99http://www.ai.mit.edu/projects/cbcl/software-datasets/PedestrianData.html100101There is also a small number of false alarms in this sequence.102NOTE: This is per frame detection, not tracking (which is also one of103the reasons why it is not mislead by the person's shadow on the back104wall).105106On an Intel Xeon 1.7GHz machine the detectors operate at something107between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the108detector. The detectors work as well on much lower image resolutions109which is always an interesting possibility for speed-ups or110"coarse-to-fine" search strategies.111112Additional information e.g. on training parameters, detector113combination, detecting other types of objects (e.g. cars) etc. is114available in my PhD thesis report (available end of June). Check out115www.vision.ethz.ch/kruppa/116117118KNOWN LIMITATIONS119==================1201) The detectors only support frontal and back views but not sideviews.121Sideviews are trickier and it makes a lot of sense to include additional122modalities for their detection, e.g. motion information. I recommend123Viola and Jones' ICCV 2003 paper if this further interests you.1241252) Don't expect these detectors to be as accurate as a frontal face detector.126A frontal face as a pattern is pretty distinct with respect to other127patterns occurring in the world (i.e. image "background"). This is not so128for upper, lower and especially full bodies, because they have to rely129on fragile silhouette information rather than internal (facial) features.130Still, we found especially the upper body detector to perform amazingly well.131In contrast to a face detector these detectors will also work at very low132image resolutions133134Acknowledgements135================136Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.137-->138<opencv_storage>139<haarcascade_fullbody type_id="opencv-haar-classifier">140<size>14 28</size>141<stages>142<_>143<!-- stage 0 -->144<trees>145<_>146<!-- tree 0 -->147<_>148<!-- root node -->149<feature>150<rects>151<_>1 5 12 21 -1.</_>152<_>5 5 4 21 3.</_></rects>153<tilted>0</tilted></feature>154<threshold>-0.0558205693960190</threshold>155<left_val>0.5869792103767395</left_val>156<right_val>-0.6281142234802246</right_val></_></_>157<_>158<!-- tree 1 -->159<_>160<!-- root node -->161<feature>162<rects>163<_>9 2 3 26 -1.</_>164<_>9 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2.</_></rects>5313<tilted>1</tilted></feature>5314<threshold>-0.0236271601170301</threshold>5315<left_val>-0.8834797739982605</left_val>5316<right_val>9.7397705540060997e-003</right_val></_></_>5317<_>5318<!-- tree 12 -->5319<_>5320<!-- root node -->5321<feature>5322<rects>5323<_>7 17 7 2 -1.</_>5324<_>7 17 7 1 2.</_></rects>5325<tilted>1</tilted></feature>5326<threshold>-0.0139276403933764</threshold>5327<left_val>-0.6530944108963013</left_val>5328<right_val>0.0528865084052086</right_val></_></_>5329<_>5330<!-- tree 13 -->5331<_>5332<!-- root node -->5333<feature>5334<rects>5335<_>6 12 3 16 -1.</_>5336<_>6 20 3 8 2.</_></rects>5337<tilted>0</tilted></feature>5338<threshold>3.6122989840805531e-003</threshold>5339<left_val>-0.2636939883232117</left_val>5340<right_val>0.1059527993202210</right_val></_></_>5341<_>5342<!-- tree 14 -->5343<_>5344<!-- root node -->5345<feature>5346<rects>5347<_>2 24 9 4 -1.</_>5348<_>5 24 3 4 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2.</_></rects>5493<tilted>0</tilted></feature>5494<threshold>-0.0446261987090111</threshold>5495<left_val>0.3832083940505981</left_val>5496<right_val>-0.0962854698300362</right_val></_></_>5497<_>5498<!-- tree 27 -->5499<_>5500<!-- root node -->5501<feature>5502<rects>5503<_>3 10 8 4 -1.</_>5504<_>3 10 4 4 2.</_></rects>5505<tilted>0</tilted></feature>5506<threshold>1.4027470024302602e-004</threshold>5507<left_val>-0.1726175993680954</left_val>5508<right_val>0.1657430976629257</right_val></_></_>5509<_>5510<!-- tree 28 -->5511<_>5512<!-- root node -->5513<feature>5514<rects>5515<_>0 5 9 18 -1.</_>5516<_>3 11 3 6 9.</_></rects>5517<tilted>0</tilted></feature>5518<threshold>0.0391159094870090</threshold>5519<left_val>0.0786521136760712</left_val>5520<right_val>-0.3568983972072601</right_val></_></_>5521<_>5522<!-- tree 29 -->5523<_>5524<!-- root node -->5525<feature>5526<rects>5527<_>8 8 4 6 -1.</_>5528<_>8 8 2 6 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-->5751<_>5752<!-- root node -->5753<feature>5754<rects>5755<_>0 25 8 3 -1.</_>5756<_>4 25 4 3 2.</_></rects>5757<tilted>0</tilted></feature>5758<threshold>-7.1978997766564135e-006</threshold>5759<left_val>0.1624667048454285</left_val>5760<right_val>-0.3189904093742371</right_val></_></_>5761<_>5762<!-- tree 3 -->5763<_>5764<!-- root node -->5765<feature>5766<rects>5767<_>2 26 12 2 -1.</_>5768<_>2 26 6 2 2.</_></rects>5769<tilted>0</tilted></feature>5770<threshold>0.0141281802207232</threshold>5771<left_val>0.0432598814368248</left_val>5772<right_val>-0.5932887792587280</right_val></_></_>5773<_>5774<!-- tree 4 -->5775<_>5776<!-- root node -->5777<feature>5778<rects>5779<_>1 22 4 6 -1.</_>5780<_>3 22 2 6 2.</_></rects>5781<tilted>0</tilted></feature>5782<threshold>-9.5496661961078644e-003</threshold>5783<left_val>-0.6398767232894898</left_val>5784<right_val>0.0462039299309254</right_val></_></_>5785<_>5786<!-- tree 5 -->5787<_>5788<!-- root node -->5789<feature>5790<rects>5791<_>6 16 2 12 -1.</_>5792<_>6 16 1 12 2.</_></rects>5793<tilted>0</tilted></feature>5794<threshold>-2.4156800936907530e-003</threshold>5795<left_val>0.2600989937782288</left_val>5796<right_val>-0.1709903031587601</right_val></_></_>5797<_>5798<!-- tree 6 -->5799<_>5800<!-- root node -->5801<feature>5802<rects>5803<_>2 14 6 14 -1.</_>5804<_>2 14 3 7 2.</_>5805<_>5 21 3 7 2.</_></rects>5806<tilted>0</tilted></feature>5807<threshold>4.4057718478143215e-003</threshold>5808<left_val>-0.2267919927835465</left_val>5809<right_val>0.1639396995306015</right_val></_></_>5810<_>5811<!-- tree 7 -->5812<_>5813<!-- root node -->5814<feature>5815<rects>5816<_>4 8 6 6 -1.</_>5817<_>6 8 2 6 3.</_></rects>5818<tilted>0</tilted></feature>5819<threshold>-0.0338254384696484</threshold>5820<left_val>-0.7283406257629395</left_val>5821<right_val>0.0516999587416649</right_val></_></_>5822<_>5823<!-- tree 8 -->5824<_>5825<!-- root node -->5826<feature>5827<rects>5828<_>0 18 8 6 -1.</_>5829<_>0 18 4 3 2.</_>5830<_>4 21 4 3 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