This is exactly the system we used in the 2008 PASCAL VOC Challenge.
It extends the work in [1] as briefly described in [2]. A forthcoming
paper will describe the new system in full detail. The current
release contains the basic object detection code and models trained on
several PASCAL datasets. The model learning code and the context
rescoring code will be available soon.
[1] P. Felzenszwalb, D. McAllester, D. Ramanan
A Discriminatively Trained, Multiscale, Deformable Part Model
Proceedings of the IEEE CVPR 2008
pdf
[2] P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan
Discriminatively Trained Mixtures of Deformable Part Models
PASCAL VOC Challenge 2008
pdf
presentation slides
The system is implemented in matlab, with a few helper functions
implemented in C++ for efficiency reasons.
The README file has
instructions on how to compile and use the system.
This software is released under the MIT License.
To download, click here: voc-release2.tgz (updated on 10/04/08)
This project is supported by the National Science Foundation under Grant No. 0534820 and 0746569.
The models included with the source code were trained on the train+val
dataset from each year and evaluated on the corresponding test
dataset.
This is exactly the protocol of the "comp3"
competition.
2006 data | bicycle | bus | car | cat | cow | dog | horse | mbike | person | sheep |
with context | 0.653 | 0.544 | 0.636 | 0.216 | 0.428 | 0.167 | 0.442 | 0.621 | 0.401 | 0.446 |
without context | 0.643 | 0.525 | 0.641 | 0.180 | 0.408 | 0.145 | 0.389 | 0.584 | 0.382 | 0.412 |
2007 data | aero | bicycle | bird | boat | bottle | bus | car | cat | chair | cow | table | dog | horse | mbike | person | plant | sheep | sofa | train | tv |
with context | 0.306 | 0.569 | 0.022 | 0.155 | 0.279 | 0.399 | 0.479 | 0.177 | 0.178 | 0.180 | 0.251 | 0.127 | 0.495 | 0.395 | 0.360 | 0.167 | 0.183 | 0.219 | 0.367 | 0.373 |
without context | 0.278 | 0.561 | 0.014 | 0.146 | 0.257 | 0.383 | 0.470 | 0.151 | 0.164 | 0.167 | 0.231 | 0.110 | 0.444 | 0.373 | 0.355 | 0.140 | 0.169 | 0.193 | 0.319 | 0.373 |