| CALVIN group calvin upper-body detector v1.04
Marcin Eichner, Vittorio Ferrari
Overview
We release here software for human upper body detection in still images. It is based on the successful part-based object detection framework [4] and contains a model to detect near-frontal upper-bodies, trained from the data of [3]. The resulting detector returns bounding-boxes fitting the head and upper half of the torso of the person.
In order to find more people we complement the primary detector with upper-body detections regressed from the Viola-Jones [5] face detector. This is especially valuable for people in poses difficult to detect by the upper-body model (e.g. arms raised above the head). Both primary and secondary detectors are combined by this release and a single homogeneous set of upper-body bounding-boxes are returned.
The bounding-boxes returned by the detector released here can be directly fed into our pose estimation software [1], which includes a matlab routine to easily interface with this detector. By installing both this detector and [1] you get a complete and fully automatic human detection and pose estimation pipeline.
This upper-body detector improves over [3] in that:new in v1.04:
- a memory leak fixed, noticable when processing many images with no detections; thanks to Huizhong Chen for pointing that out
Example results
Example results are shown on the gallery website of our online human pose estimation demo which employs this detector.
Performance
this detector has been evaluated in our Technical Report [8]
Training data
The upper-body detector was trained from the data of [3].
Downloads
| Filename | Description | Size |
|---|---|---|
| calvin_upperbody_detector_v1.04.tgz | calvin upper-body detector | 223 kB |
| README.html | description of contents | 31 kB |
| voc-release3.1.tgz | snapshot of the object dectection framework [4] required by our upper-body detector | 7200 kB |
References
[1] Eichner, M. and Ferrari, V.
2d articulated human pose estimation code
http://www.vision.ee.ethz.ch/~calvin/articulated_human_pose_estimation_code/
[2] Eichner, M. and Ferrari, V.
Better Appearance Models for Pictorial Structures
Proceedings of British Machine Vision Conference (BMVC), 2009.
Document: PDF
[3] M. Marin, V. Ferrari, A. Zisserman
upper-body detector
www.robots.ox.ac.uk/~vgg/software/UpperBody/
[4] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan
Object Detection with Discriminatively Trained Part Based Models
Pattern Recognition and Machine Learning (PAMI), 2009
[5] P. Viola, M. Jones
Rapid Object Detection using a Boosted Cascade of Simple Features
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2001
[6] OpenCV computer vision library
http://opencv.willowgarage.com/wiki/
[7] N. Dalal and B. Triggs
Histograms of Oriented Gradients for Human Detection
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2005
[8] M.Eichner, M. Marin-Jimenez, A. Zisserman, V.Ferrari
Articulated Human Pose Estimation and Search in (Almost) Unconstrained Still Images
ETH Zurich, D-ITET, BIWI, Technical Report No.272, September 2010.
Document: PDF
Acknowledgements
We thank Pedro Felzenszwalb, David McAllister and Deva Ramanan for allowing us to host their object detection framework [4] on our website.
We thank Manuel Marin for the training data released on [3].
We thank Alessandro Prest for his contribution into the training of the part-based upper-body model
Finally we thank Pietro Perona for challening us with images of his group during a talk at Caltech. This prompted us to improve the portability and performance of the detector.