Volume & Issue no: Volume 9, Issue 5, September - October 2020
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Title: |
Dual superpixel HOG Pedestrian Detector |
Author Name: |
Daniel A. Mitchell, Harvey B. Mitchell |
Abstract: |
Abstract: The Histogram-of-Oriented-Gradient (HOG) is a
widely used feature used in many pattern recognition
applications involving pedestrian detection. The basic idea of
HOG is that the local pedestrian appearance can be
characterized by the distribution of local intensity gradients and
edge directions. In this letter we describe a dual superpixel
HOG algorithm in which we fuse together two HOG feature
vectors. The first vector is the traditional HOG feature vector
calculated on the input image. The second vector is a HOG
feature vector which is calculated on the input image after
superpixel segmentation. By fusing the two HOG vectors
together we obtain a fused HOG with an enhanced performance
while at the same time being fully compatible with the
traditional HOG. Experimental results on standard pedestrian
detection databases show that for noisy input the dual HOG
significantly outperforms the traditional HOG detector.
Keywords: Histogram-of-oriented-gradient, Pedestrian
detection, superpixel, HOG, image processing |
Cite this article: |
Daniel A. Mitchell, Harvey B. Mitchell , "
Dual superpixel HOG Pedestrian Detector" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 9, Issue 5, September - October 2020 , pp.
067-071 , ISSN 2278-6856.
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