Count Method of Leaves on the Surface of a River using Interactive Color Indication of Coloring Picture Style

F. Tsutsumi and Y. Tateda (Japan)


Video image processing, Color image processing, Environ mental monitoring, Interactive machine learning


We propose a count method for leaves on the surface of a river to be used in ecosystem monitoring. Since conven tional count methods require considerable manual labor for precise monitoring of material flow in the ecosystem, an efficient counting method was needed. Our method auto matically counts the number of floating leaves in recorded video using color and motion features. The color feature is represented by 3 dimensional histogram of a color space. We have developed a user interface based on the interactive machine learning model to extract the color feature from video images. The user can easily produce a huge number of sample data to extract the color feature by the user inter face in the same way as coloring a picture. For the motion feature, speed and acceleration of the targets are used. The counting method proposed in this paper has been applied to three videos (total five hours) which recorded about 20,000 leaves, and high recall and precision rates of 96% and 94%, respectively, have been achieved.

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