An Object Detection Algorithm based on Deformable Part Models with Bing Features

DOI

10.2991/icamcs-16.2016.164How to use a DOI?

Keywords

Object detection, deformable part models, binarized normed gradients feature.

Abstract

To solve the problem that the positioning strategy with sliding window approaches requires exhaustive search in feature pyramids, the paper proposes an object detection algorithm based on deformable part models with Bing features to help object detection. First of all, input images are preprocessed with the objectness detection algorithm with Bing features and a set of potential windows that may contain target objects are obtained, and then the deformable part model is regarded as the class-specific detector to match potential windows, at last Non-Maximum Suppression is used to merge and reduce window areas of results to obtain final detection results. The experimental results on Pascal VOC 2007 dataset show that the algorithm in the paper outperforms the original DPM in 19 out of 20 classes, achieving an improvement of 2.7% mAP.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chunwei Li
AU  - Youjun Bu
PY  - 2016/06
DA  - 2016/06
TI  - An Object Detection Algorithm based on Deformable Part Models with Bing Features
BT  - Proceedings of the 2016 5th International Conference on Advanced Materials and Computer Science
PB  - Atlantis Press
SP  - 803
EP  - 809
SN  - 2352-5401
UR  - https://doi.org/10.2991/icamcs-16.2016.164
DO  - 10.2991/icamcs-16.2016.164
ID  - Li2016/06
ER  -