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Object Detection with Discriminatively Trained Part Based Models

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by Pedro F. Felzenszwalb , Ross B. Girshick , David McAllester , Deva Ramanan
Citations:1422 - 49 self
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BibTeX

@MISC{Felzenszwalb_objectdetection,
    author = {Pedro F. Felzenszwalb and Ross B. Girshick and David McAllester and Deva Ramanan},
    title = {Object Detection with Discriminatively Trained Part Based Models},
    year = {}
}

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Abstract

We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their value had not been demonstrated on difficult benchmarks such as the PASCAL datasets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM is a reformulation of MI-SVM in terms of latent variables. A latent SVM is semi-convex and the training problem becomes convex once latent information is specified for the positive examples. This leads to an iterative training algorithm that alternates between fixing latent values for positive examples and optimizing the latent SVM objective function.

Keyphrases

object detection    discriminatively trained part    latent svm    positive example    deformable part model    discriminative training    new method    object detection system    multiscale deformable part model    latent svm objective function    data-mining hard negative example    latent information    iterative training algorithm    difficult benchmark    latent variable    pascal object detection challenge    latent value    variable object class    training problem    state-of-the-art result    pascal datasets    margin-sensitive approach   

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