3 19x23 lowerbody detector (see the detailed description below).
5 //////////////////////////////////////////////////////////////////////////
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41 //////////////////////////////////////////////////////////////////////////
43 "Haar"-based Detectors For Pedestrian Detection
44 ===============================================
45 by Hannes Kruppa and Bernt Schiele, ETH Zurich, Switzerland
47 This archive provides the following three detectors:
48 - upper body detector (most fun, useful in many scenarios!)
52 These detectors have been successfully applied to pedestrian detection
53 in still images. They can be directly passed as parameters to the
54 program HaarFaceDetect.
55 NOTE: These detectors deal with frontal and backside views but not
56 with side views (also see "Known limitations" below).
59 If you are using any of the detectors or involved ideas please cite
60 this paper (available at www.vision.ethz.ch/publications/):
62 @InProceedings{Kruppa03-bmvc,
63 author = "Hannes Kruppa, Modesto Castrillon-Santana and Bernt Schiele",
64 title = "Fast and Robust Face Finding via Local Context."
65 booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
71 If you have any commercial interest in this work please contact
75 ADDITIONAL INFORMATION
76 ======================
77 Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player
78 that can play back .mpg movies.
84 The movie shows a person walking towards the camera in a realistic
85 indoor setting. Using ffplay or mplayer you can pause and continue the
86 movie by pressing the space bar.
88 Detections coming from the different detectors are visualized using
89 different line styles:
90 upper body : dotted line
91 lower body : dashed line
92 full body : solid line
94 You will notice that successful detections containing the target do
95 not sit tightly on the body but also include some of the background
96 left and right. This is not a bug but accurately reflects the
97 employed training data which also includes portions of the background
98 to ensure proper silhouette representation. If you want to get a
99 feeling for the training data check out the CBCL data set:
100 http://www.ai.mit.edu/projects/cbcl/software-datasets/PedestrianData.html
102 There is also a small number of false alarms in this sequence.
103 NOTE: This is per frame detection, not tracking (which is also one of
104 the reasons why it is not mislead by the person's shadow on the back
107 On an Intel Xeon 1.7GHz machine the detectors operate at something
108 between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the
109 detector. The detectors work as well on much lower image resolutions
110 which is always an interesting possibility for speed-ups or
111 "coarse-to-fine" search strategies.
113 Additional information e.g. on training parameters, detector
114 combination, detecting other types of objects (e.g. cars) etc. is
115 available in my PhD thesis report (available end of June). Check out
116 www.vision.ethz.ch/kruppa/
121 1) the detectors only support frontal and back views but not sideviews.
122 Sideviews are trickier and it makes a lot of sense to include additional
123 modalities for their detection, e.g. motion information. I recommend
124 Viola and Jones' ICCV 2003 paper if this further interests you.
126 2) dont expect these detectors to be as accurate as a frontal face detector.
127 A frontal face as a pattern is pretty distinct with respect to other
128 patterns occuring in the world (i.e. image "background"). This is not so
129 for upper, lower and especially full bodies, because they have to rely
130 on fragile silhouette information rather than internal (facial) features.
131 Still, we found especially the upper body detector to perform amazingly well.
132 In contrast to a face detector these detectors will also work at very low
137 Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
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