Development on an image processing system for traffic flow analysis
Grant-in-Aid for Developmental Scientific Research.
|Allocation Type||Single-year Grants|
|Research Institution||Hokkaido University|
KAKU Terutoshi Hokkaido Univ., Fac.of Eng., Professor, 工学部, 教授 (40001135)
HAGIWARA Toru Hokkaido Univ., Fac.of Eng., Instructor, 工学部, 助手 (60172839)
FIJIWARA Takashi Hokkaido Univ., Fac.of Eng., Instructor, 工学部, 助手 (50109493)
NAKATUJI Takashi Hokkaido Univ., Fac.of Eng., Lecturer, 工学部, 講師 (60123949)
|Project Period (FY)
1989 – 1990
Completed(Fiscal Year 1990)
|Budget Amount *help
¥4,200,000 (Direct Cost : ¥4,200,000)
Fiscal Year 1990 : ¥900,000 (Direct Cost : ¥900,000)
Fiscal Year 1989 : ¥3,300,000 (Direct Cost : ¥3,300,000)
|Keywords||Image processing system / Traffic flow analysis / Detecting vehicles / 車両の軌跡 / 交通流パラメ-タ / 動画像処理システム / デジタル画像処理|
1. An automatic processing system for digitizing sequential pictures.
We developed an automatic processing system which detects and tracks moving vehicles from video image recorded by the TV camera. Two computers are used in this system.
1) System for digitizing video image
One computer is used to store video images. The video signal from a TV camera are digitized to a 6-bit number by an A/D converter. The entire frame is divided into 256**256 points, but only a specified region of the frame is stored in the computer's memory.
2) System for control video deck
Another computer is used to control video deck. We need to control a value of frame rate automatically. Because, the maximum frame rate is limited by access time and transfer rate and it is about 15 frames/second.
It is required to analyze the microscopic behavior of vehicles at signalized intersections during winter. In winter, road surface became very slippery, and capacity of signalized intersections in winter became less than that in other seasons. We applied a new system to study two-dimensional measurement of individual vehicle at signalized intersections. We could measure values of speed and acceleration on slippery road conditions. But. some problems remain to be solved for the practical applications in general conditions.
3. Model for detecting vehicles
We try to study a method to identify the same vehicle on two consecutive video frames. One of the Neural-Network-Model. Congitron was used to identify vehicle images. Application possibilities of cognitron for recognition of vehicle images were tested. Experimental results show that trained patterns could be recognized, but non-trained ones couldn't.
Research Output (15results)