A DISCRIMINATIVELY TRAINED MULTISCALE DEFORMABLE PART MODEL PDF

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For IEEE to continue sending you helpful information on our products and services, please consent to our updated Privacy Policy. Email Address. Sign In. A discriminatively trained, multiscale, deformable part model Abstract: 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 PASCAL person detection challenge.

It also outperforms the best results in the 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. 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. Article :. DOI: Need Help?

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