Count Method of Leaves on the Surface of a River Using Image Processing

Abstract

This paper proposes a new counting method for floating leaves on the surface of a river to be used in ecosystem monitoring. Since conventional counting methods (such as the litter trap method) require considerable manual labor for precise monitoring of material flow in ecosystem, an efficient counting method was needed. Our method automatically counts the number of floating leaves in recorded video images. Floating leaves are detected using color and motion features. The color feature is represented by 3 dimensional histograms of the RGB color space. For the motion feature, speed and acceleration of the targets are used. The counting method proposed in this paper has been applied to a five hours video which recorded 20,000 leaves, and high recall and precision rates of 96% and 94%, respectively, have been achieved. This paper also proposes a user interface for training the counting mechanism based on the Interactive Machine Learning model. The user can easily produce a huge number of sample data to train the detection mechanism by using the user interface in the same way as coloring a picture.