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Pedestrian Detection using HOG-based Block Selection
Minsung Kang and Young Chul Lim
IT Convergence Research Division, DGIST, Daegu, Hyeonpung Myeon, Korea
Keywords: Pedestrian Detection, Intelligent Vehicle, HOG, Camera, Computer Vision.
Abstract: Recently, pedestrian detection methods have been popularly used in the field of intelligent vehicles. In most
previous works, the Histogram of Oriented Gradients (HOG) is used to extract features for pedestrian
detection. However HOG is difficult to use in the real-time operating system of an intelligent vehicle. In this
paper, we proposed a pedestrian detection method using a HOG-based block selection. First, we analyse the
HOG block and select the parts of the block with a high hit rate. We then use only 20% of the total HOG
blocks for the pedestrian feature. The proposed method is 5 times faster than methods using the entire
feature, while performance remains almost the same.
1 INTRODUCTION
Pedestrian detection methods have been used
recently for intelligent vehicle, intelligent robot and
video security applications. Pedestrian detection is
the technical methodology for finding the position of
pedestrians from a camera image. In the detection
process, first, a feature is extracted for pedestrian
classification. Then a pedestrian is detected using a
feature from a searched image. The performance and
computation speed are typically different when
using features that are extracted with various shapes.
The Histogram of Oriented Gradients (HOG) is
one of the well-known features used for pedestrian
detection. The HOG feature is robust to variations of
illumination. However, The HOG feature needs a
high amount of image processing because the
dimensions of the feature are high. Hence,
pedestrian detection based on HOG is impractical
for the real time operation of vehicles. In an
intelligent vehicle, real time operation is important
because reaction time is directly connected to the
safety of the driver and pedestrian.
As a result, many pedestrian detection methods
based on the HOG feature are being researched with
the goal of reducing computation time. Many of
these existing methods change the process of
searching the image to reduce computation time.
Other methods use a GPU to improve computation
speed but these need an NVIDIA graphic card. Such
methods use the high dimensions of HOG and
improve computation speed in post-processing.
However these methods do not solve the
fundamental problem.
Accordingly, this paper proposes a pedestrian
detection method using HOG-based block selection.
The structure of this paper is as follows: Chapter 2
introduces related works about pedestrian detection.
Chapter 3 describes the proposed algorithm. Chapter
4 deals with the verification of the proposed
algorithm through experiments. And finally Chapter
5 presents conclusions.
2 RELATED WORKS
Pedestrian detection methods involve the extraction
of features from a pedestrian dataset and a training
feature using a classifier such as SVM. Then the
feature is used to detect the pedestrian in a whole
camera image. Existing methods typically change
the process of searching the image to reduce
computation time. As shown in Figure 1, the sliding
window method makes an image pyramid from the
original image in order to search the image.
The computation speed of the sliding window
method is very slow because the area being searched
is big. The classifier of the sliding window method
is fixed. Hence, to address this issue, as shown in
Figure 2, an alternative method makes various sizes
of classifier to improve the search speed. But this
method is hard to use because HOG is an invariant
feature. For this reason, a hybrid method has been
proposed, as shown in Figure 3. The computational
783
Kang M. and Lim Y..
Pedestrian Detection using HOG-based Block Selection.
DOI: 10.5220/0005147607830787
In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (IVC&ITS-2014), pages 783-787
ISBN: 978-989-758-040-6
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)