TITLE:
A Recognition Method of Pedestrians’ Running in the Red Light Based on Image
AUTHORS:
Min Zhang, Chao Li Wang, Yun Feng Ji
KEYWORDS:
SVM Classifier, Histogram, Background Modeling, Objects Tracking
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.7 No.5,
May
28,
2014
ABSTRACT: It is dangerous for pedestrians to run when the traffic shows a red
light, but in some cases the pedestrians are breaking the rules. This system
will be a meaningful thing if the jaywalking behaviors of pedestrians in the
road crossing through the monitoring cameras could be recognized. Then drivers
can be informed of the situations in advance, and they can take some actions to
avoid an accident. The characteristic behavior is the non-construction, and
furthermore, due to the change of sunlight, temperature, and weather in the
outside environment, and the shaking of cameras themselves, the background
images will change as time goes by, which will bring special difficulties in
recognizing jaywalking behaviors. In this paper, the method of adaptive
background model of mixture Gaussian is used to extract the moving objects in
the video. On the base of Histograms of Oriented Gradients (HOG), the
pedestrians images and car images from MIT Library are used to train our
monitoring system by SVM classifier, and identify the pedestrians in the video.
Then, the color histogram, position information and the movement of pedestrians
are selected to track them. After that we can identify whether the pedestrians
are running in the red lights or not, according to the transportation signals
and allocated walking areas. The experiments are implemented to show that the
proposed method is effective.