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- <?xml version="1.0"?>
- <!--
- 19x23 lowerbody 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.
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- | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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- //////////////////////////////////////////////////////////////////////////
- "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) Don't 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 occurring 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.
- -->
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