| 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
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://groups.inf.ed.ac.uk/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.org/
[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 challenging us with images of his group during a talk at Caltech. This prompted us to improve the portability and performance of the detector.