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Proceedings Paper

Fast object detection algorithm based on HOG and CNN
Author(s): Tongwei Lu; Dandan Wang; Yanduo Zhang
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Paper Abstract

In the field of computer vision, object classification and object detection are widely used in many fields. The traditional object detection have two main problems:one is that sliding window of the regional selection strategy is high time complexity and have window redundancy. And the other one is that Robustness of the feature is not well. In order to solve those problems, Regional Proposal Network (RPN) is used to select candidate regions instead of selective search algorithm. Compared with traditional algorithms and selective search algorithms, RPN has higher efficiency and accuracy. We combine HOG feature and convolution neural network (CNN) to extract features. And we use SVM to classify. For TorontoNet, our algorithm's mAP is 1.6 percentage points higher. For OxfordNet, our algorithm's mAP is 1.3 percentage higher.

Paper Details

Date Published: 10 April 2018
PDF: 5 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 1061509 (10 April 2018); doi: 10.1117/12.2302964
Show Author Affiliations
Tongwei Lu, Wuhan Institute of Technology (China)
Hubei Provincial Key Lab. of Intelligent Robot (China)
Dandan Wang, Wuhan Institute of Technology (China)
Hubei Provincial Key Lab. of Intelligent Robot (China)
Yanduo Zhang, Wuhan Institute of Technology (China)
Hubei Provincial Key Lab. of Intelligent Robot (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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