1993 Fiscal Year Final Research Report Summary
Research on Traffic Flow Study Using Image Processing Method
| Project/Area Number |
04650477
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
交通工学・国土計画
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| Research Institution | Utsunomiya University |
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Principal Investigator |
KOIKE Hirotaka Utsunomiya University, Civil Engineering Department, Professor, 工学部, 教授 (70178177)
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| Project Period (FY) |
1992 – 1993
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| Keywords | Traffic Flows / Image PRocessing / Video Camera / Automatic Vehicle Recognition / Differential Growth Method |
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| Research Abstract |
The purpose of this research is to develop a system to analyze traffic flow using image processing techniques. The system consists of video camera, VTR, NTSC decoder, image processor, minicomputer and page printer. Two methods were developed to extract vehicles from video image data. In both methods, image processing techniques were adopted to identify the location of cars on the video screen, and automatically digitize their coordinates.
The first method is a subtract operation between two consecutive images, which leaves only the moving parts as residual images on the screen. Then the center of gravity of these residual images are computed. The problem with this method is the inaccuracy of center of gravity of a car when shadow exists or when a number of cars closely positioned.
In the second method, a new approach was developed to further improve the accuracy of automatic recognition of cars using a procedure which is named as the differential growth method. The procedure works as follows. First, make the residual of subtracted images which shows the part of moving cars. Then applying the new method, the partial image of the car is led to grow to restore the original car shape. Once the original car image is restored, accurate center of gravity is computed and the trajectory of car movement is traced, or its speed is measured.
Using this procedure, much more accurate movements of vehicles are automatically recognized. Case study at an intersection showed vehicle movement in the intersection fairly accurately. The application of this method seems promising in various traffic engineering surveys.
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Research Products
(8 results)
