A discriminatively trained, multiscale, deformable part model


This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge. It also outperforms the best results in the 2007 challenge in ten out of twenty categories. The system relies heavily on deformable parts. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL challenge. Our system also relies heavily on new methods for discriminative training. We combine a margin-sensitive approach for data mining hard negative examples with a formalism we call latent SVM. A latent SVM, like a hidden CRF, leads to a non-convex training problem. However, a latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. We believe that our training methods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.

DOI: 10.1109/CVPR.2008.4587597
View Slides

Extracted Key Phrases

7 Figures and Tables

Citations per Year

1,651 Citations

Semantic Scholar estimates that this publication has 1,651 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Felzenszwalb2008ADT, title={A discriminatively trained, multiscale, deformable part model}, author={Pedro F. Felzenszwalb and David A. McAllester and Deva Ramanan}, journal={2008 IEEE Conference on Computer Vision and Pattern Recognition}, year={2008}, pages={1-8} }