| Research Abstract |
Last fiscal year, we developed an algorithm which is able to track both rigid objects such as vehicles and flexible objects such as pedestrians in images.
This fiscal year, improving this tracking algorithm, we enabled to get detail trajectory of these various types of moving objects, and implemented a prototype of Real-time System.
On the other hand, we developed a system which detects events based on experiments mainly conducted on Metropolitan Expressway.
By applying the Spatio-Temporal trajectory of each object from the algorithm, this system classifies traffic events by using a rule-based reasoning method.
First Coordinate-class operator extracts Spatio-Temporal trajectory of each vehicle from tracking results.
Secondly, Behavior-class operator extracts behavior of each vehicle such as "Stall" and "Stop" from the Spatio-Temporal trajectories.
Lastly, using behaviors information of vehicles on the image, Event-class operator detects traffic events such as accidents, congestion, etc.
As above, our event detection, system utilizes a semantic hierarchy consisting of three kinds of operators (Coordinate-class, Behavior-class and Event-class) and has high affinity with the human cognitive process.
So, it can be said that development of algorithm which tracks vehicles and pedestrians simultaneously achieved some positive success. When it came to the event detection, fundamental algorithm was developed to recognize behaviors of vehicles in particular. We consider that this algorithm can also be applied to images including vehicles and pedestrians, and will continue the verification of the system extensibility.
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