An important experiment to test effect of new drug in medical science, and brain science is to study rat behaviors. Open-field test such as holeboard model is popular experiment model to analyze rat behavior. Number of each rat behavior in a period of time, example, walking, rearing, and head dip is counted and recorded. This data is compared between before and after using the drug with rat. In present, these rat behaviors are observed and counted by experimenters, that, obviously, it is included human errors easily. In this paper, we proposed a method for improving rat walking behavior classification accuracy by local foreground extraction technique.
Webcam is used to record rat behavior and it is mounted over the model 1.5 meters. The proposed method consists of four steps. First, background is constructed for background subtraction by K-Mean clustering technique. Second, foreground as rat is extracted by background subtraction technique. Third step is local extraction. It is applied to complete rat body data from the second step. Finally, rat body length measured by ellipse fitting technique is used for walking behavior classification. To evaluate performance of the proposed method, classification accuracy is measured. Accuracy rate of the proposed method is 83.8%. Results show that accuracy rate of the proposed method is higher than existing method. An advantage of the proposed method is that it improves the accuracy from the existing research.