Yuhei Yamamoto

In Japan, the Sport Basic Plan was formulated in 2012, which mentions not only the development of highly qualified sports instructors but also new perspectives such as the provision of programs in which everyone can enjoy the value of sports together. Against this background, trials of new technologies utilizing the latest ICT equipment such as sensor devices are being made. However, in college sports, where financial resources are often limited, shooting with hand-held video cameras is the most common method, which may cause the manager to overlook important scenes of play depending on his or her skill level. This problem affects not only competitors but also spectators. To solve this problem, we develop a system for capturing video images of the entire field with multiple fixed video cameras so that the system can automatically switch from one to another video of the most appropriate camera for the respective scenes of play. The results of the demonstration experiments in basketball and futsal showed that the switching video of the proposed technology can be utilized for tactical analysis in sports.

In our country, AI-based survey methods are being promoted to streamline traffic volume surveys. Existing research has shown that vehicle section identification using AI can count sectional traffic volumes by vehicle type, but accuracy decreases due to flares and scenery reflections. Additionally, vehicle types can be determined by recognizing the leading number of the classification code on license plates, but the recognition accuracy decreases when the characters are unclear. Therefore, this study proposes a method to determine vehicle types by recognizing classification codes when the characters are clear and by using vehicle section identification results when the characters are unclear. Applying the proposed method to videos taken at three different locations resulted in a high accuracy with an F-measure of 0.95 or higher at all locations. In the future, we aim for early practical application in automotive traffic volume surveys by accommodating license plates with designs.

Recently, there have been many cases of analyzing traffic volume and travel speed per route using probe data collected from automobiles. In addition, if the behavior of individual vehicles could be understood in terms of driving lanes, more advanced road traffic analysis could be realized, such as analyzing the behavior of lane changes at mergers and crossings. In this study, we devised a method to estimate the lane of driving of individual vehicles from ETC2.0 probe data, which has higher positioning accuracy than ETC2.0 probe data, by superimposing the driving trajectories of individual vehicles on a mesh divided by lane. Then, machine learning was applied to the time-series changes in the unique numbers assigned to the mesh. The results suggest that although the estimation results are affected by the positioning accuracy of ETC2.0 probe data, it is possible to estimate the driving lane of an individual vehicle with higher accuracy than when the positioning points are checked one by one.

In Japan, AI is being incorporated into automobile traffic volume surveys to enhance efficiency. By recognizing license plates in these surveys, it's possible to measure vehicle flow, refining the accuracy of the survey. However, when the video footage is taken from a high vantage point on the road's shoulder, the license plates appear tilted. To correct this, the image needs to be adjusted to a frontal orientation. If the license plate and the vehicle have the same color, determining a reference point for the projective transformation becomes challenging. To address this issue, we developed a method that corrects the license plate to a front-facing orientation using a projective transformation, based on the position of the fourth digit of the serial number—a consistent feature on all license plates. Our experimental results indicated successful character extraction from 479 out of 500 images. We aim to apply this technique in future traffic flow studies to identify the same vehicle at multiple locations.