A Hierarchical Extension of the HOG Model Implemented in the Convolution-net for Human Detection

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概要

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• For the detection of generic objects in the field of image processing, histograms of orientation gradients (HOG) is discussed for these years. The performance of the classification system using HOG shows a good result. However, the performance of using HOG descriptor would be influenced by the detecting object size. In order to overcome this problem, we introduce a kind of hierarchy inspired from the convolution-net, which is a model of our visual processing system in the brain. The hierarchical HOG (H-HOG) integrates several scales of HOG descriptors in its architecture, and represents the input image as the combinatorial of more complex features rather than that of the orientation gradients. We investigate the H-HOG performance and compare with the conventional HOG. In the result, we obtain the better performance rather than the conventional HOG. Especially the size of representation dimension is much smaller than the conventional HOG without reducing the detecting performance.

著者

• Morie Takashi

  Kyushu Inst. Of Technol. Kitakyushu‐shi Jpn

• Shouno Hayaru

  University of Electro-Communications

• Arakaki Yasuto

  University of Electro-Communications

• Takahashi Kazuyuki

  Kyushu Institute of Technology

関連論文

• An Image-Filtering LSI Processor Architecture for Face/Object Recognition Using a Sorted Projection-Field Model Based on a Merged/Mixed Analog-Digital Architecture(Analog Circuit and Device Technologies)
• A Hierarchical Extension of the HOG Model Implemented in the Convolution-net for Human Detection

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