Abstract
Abstract
Because of advantage of the bus, it has been accepted by the general public and
as the preferred means of transport to travel the city. But now there are many public
transport problems needed to be solved urgently, such as unreasonable urban route
planning, unscientific dispatch during the morning and evening peak, impertinent
station distribution of sparse or tightness, frequent traffic rub accidents in flow traffic
etc, so development of modern intelligent transportation systems for solving the
problems of these buses has a very important practical significance. In the scientific
comparatively transportation system, how to get high accuracy and real-time traffic
information is particularly important. With accurate and real-time passenger
information, not only scheduling the line bus can be more scientific, but also helping
the bus company to make a more reasonable arrangement about lines and sites. In
addition, these accurate and real-time traffic information can also create special
economic and social benefits in the era of big data.
Currently we are increasingly able to reach this consensus: Since the image
contains a huge amount of information, the use of image processing techniques to
count traffic statistics in the real-time public transport system has become a important
development direction. One-dimensional monocular vision and multi-dimensional or
two-dimensional binocular vision are two branches of passenger statistics which are
based the image processing technology .In consequence monocular vision technology
for processing image is easy to be affected by the intensity changes of color light and
cover and other factors, now we use extensively stereoscopic technology to make
image processing. This study utilizes stereo vision technology to design algorithm of
getting the transit passenger flow information. Finally the algorithm is achieved on
SBC3730 platforms.
This research firstly elaborates stereo matching technique, and analyzes several
matching algorithms, and selects dynamic programming algorithm to obtain the
disparity map according to the real-time and accurate requirements. The dense
disparity map which is obtained by the method of processing globally is smoother
than the other methods Then this research use the way which combining background
subtraction model and optical flow residual to detect moving objects, and segment the
target, and use the associated field marks to locate and extract target in moving state.
Multi-feature fusion tracking algorithm is applied for target tracking count in this
万方数据
