Publication: Local foreground extraction technique for rat walking behavior classification
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Issued Date
2015
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-84925854600
Rights Holder(s)
Scopus
Bibliographic Citation
Proceedings of the 2015-7th International Conference on Knowledge and Smart Technology, KST 2015. (2015), p.54-59
Suggested Citation
Chanchanachitkul W., Nanthiyanuragsa P., Sangpayap R., Thongsaard W., Charoenpong T. Local foreground extraction technique for rat walking behavior classification. Proceedings of the 2015-7th International Conference on Knowledge and Smart Technology, KST 2015. (2015), p.54-59. doi:10.1109/KST.2015.7051461 Retrieved from: https://hdl.handle.net/20.500.14740/6216
Abstract
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. © 2015 IEEE.