International Core Journal of Engineering 2020-26 | Page 102

(a)
(c)
V. C ONCLUSION
In this paper, to improve the performance in moving
targets detection from video frames, an inter-frame
difference method is proposed by using the combination of
both texture features and color features. From the
experimental results, it is found that the texture features are
crucial in moving target detection, while color features can
act as a good supplement for moving targets detection.
Extensive experiments demonstrate that the proposed method
can successfully detect the moving targets from the video
datasets. The reason may be that the color features and the
texture features represent two different aspects of the images,
while the texture features detected by the Gabor filter are
susceptible to environmental changes. Our future work is to
improve the proposed method by making full use of the
continuity in the video frames.
( (b) )
(d)
(e)
Fig. 7. The experiment results for Highway dataset. (a): The original video
frame; (b): The extracted moving targets frame; (c) Detection result:
Bitmap_Gabor; (d) Detection result: Bitmap_Lab; (e): Combined detection
result: Binary_image.
(a)
A CKNOWLEDGMENT
This work is supported by the National Key R&D
Program of China (Grants No. 2017YFE0111900,
2018YFB1003205).
(b)
R EFERENCES
[1]
(c)
(d)
[2]
(d)
Fig. 8. The experiment results for Office dataset. (a): The original video
frame; (b): The extracted moving targets frame; (c) Detection result:
Bitmap_Gabor; (d) Detection result: Bitmap_Lab; (e): Combined detection
result: Binary_image.
[3]
[4]
[5]
[6]
(a)
(b)
[7]
[8]
(c)
(d)
(e)
[9]
Fig. 9. The experiment results for Pedestrian dataset. (a): The original video
frame; (b): The extracted moving targets frame; (c) Detection result:
Bitmap_Gabor; (d) Detection result: Bitmap_Lab; (e): Combined detection
result: Binary_image.
[10]
[11]
[12]
(a)
(c)
(b)
(d)
[13]
[14]
(e)
Fig. 10. The experiment results for PETS2006 dataset. (a): The original
video frame; (b): The extracted moving targets frame; (c) Detection result:
Bitmap_Gabor; (d) Detection result: Bitmap_Lab; (e): Combined detection
result: Binary_image
[15]
[16]
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