A discriminatively trained, multiscale, deformable part model

This paper describes a discriminatively trained, multi- scale, deformable part model for object detection. Our sys- tem achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person de- tection 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 calllatent SVM. A latent SVM, like a hid- den CRF, leads to a non-convex training problem. How- ever, a latent SVM is semi-convex and the training prob- lem becomes convex once latent information is specified for the positive examples. We believe that our training meth- ods will eventually make possible the effective use of more latent information such as hierarchical (grammar) models and models involving latent three dimensional pose.