A DISCRIMINATIVELY TRAINED MULTISCALE DEFORMABLE PART MODEL PDF

This paper describes a discriminatively trained, multiscale, deformable part model for object detection. Our system achieves a two-fold improvement in average. This paper describes a discriminatively trained, multi- scale, deformable part model for object detection. Our sys- tem achieves a two-fold. “A discriminatively trained, multiscale, deformable part model.” Computer Vision and Pattern Recognition, CVPR IEEE Conference on. IEEE,

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A discriminatively trained, multiscale, deformable part model

FelzenszwalbDavid A. 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 discrkminatively deformable parts. This paper has highly influenced other papers.

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This paper has 2, citations.

A Discriminatively Trained, Multiscale, Deformable Part Model | BibSonomy

From This Paper Topics from this paper. Topics Discussed in This Paper. Discriminative model Data mining Object detection.

Pascal Information retrieval Semantics computer science. Semiconductor industry Latent Dirichlet allocation Conditional random field. Citations Publications citing this paper.

A discriminatively trained, multiscale, deformable part model – Semantic Scholar

Showing of 1, extracted citations. CorsoKhurshid A. Face detection based on deep convolutional neural networks exploiting incremental facial part learning Danai TriantafyllidouAnastasios Tefas 23rd International Conference on Pattern….

Fast moving pedestrian detection based on motion segmentation and new motion features Shanshan ZhangDominik A. KleinChristian BauckhageArmin B. Cremers Multimedia Tools and Applications Citation Statistics 2, Citations 0 ’10 ’13 ’16 ‘ Semantic Scholar estimates that this publication has 2, citations based on the available data. See our Aprt for additional information.

References Publications referenced by this paper. Showing of 23 references. Patchwork of parts models for object recognition. Making large – scale svm learning practical. By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy PolicyTerms of Serviceand Dataset License.

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