TY - GEN
T1 - Image processing and behavior planning for robot-rat interaction
AU - Shi, Qing
AU - Ishii, Hiroyuki
AU - Konno, Shinichiro
AU - Kinoshita, Shinichi
AU - Takanishi, Atsuo
PY - 2012
Y1 - 2012
N2 - In this paper, we proposed an automated video processing system to replace the traditional manual annotation, and to improve the adaptivity of the rat-like robot to autonomously interact with rats. The feature parameters of rats, such as body length, body area, circularity, body bend angle, locomotion speed, etc., are extracted based on image processing. These parameters are integrated as the input feature vector of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classification methods respectively. Preliminary experiments reveal that the rearing, grooming and rotating actions could be recognized with extremely high rate (more than 90% by SVM and more than 80% by ANN). Furthermore, SVM needs less training computational cost than ANN. Therefore, SVM is superior to ANN for the behavior recognition of rats. By using the SVM-based recognition system, the behavior of the robot is generated adaptive to the rat behavior for different interactions.
AB - In this paper, we proposed an automated video processing system to replace the traditional manual annotation, and to improve the adaptivity of the rat-like robot to autonomously interact with rats. The feature parameters of rats, such as body length, body area, circularity, body bend angle, locomotion speed, etc., are extracted based on image processing. These parameters are integrated as the input feature vector of Artificial Neural Network (ANN) and Support Vector Machine (SVM) classification methods respectively. Preliminary experiments reveal that the rearing, grooming and rotating actions could be recognized with extremely high rate (more than 90% by SVM and more than 80% by ANN). Furthermore, SVM needs less training computational cost than ANN. Therefore, SVM is superior to ANN for the behavior recognition of rats. By using the SVM-based recognition system, the behavior of the robot is generated adaptive to the rat behavior for different interactions.
UR - http://www.scopus.com/inward/record.url?scp=84867421399&partnerID=8YFLogxK
U2 - 10.1109/BioRob.2012.6290292
DO - 10.1109/BioRob.2012.6290292
M3 - Conference contribution
AN - SCOPUS:84867421399
SN - 9781457711992
T3 - Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
SP - 967
EP - 973
BT - 2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
T2 - 2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2012
Y2 - 24 June 2012 through 27 June 2012
ER -