Caged mice mating behavior detection in surveillance videos

Abstract The purpose of this study is to develop a computer vision-based method to automatically detect the mating behavior of caged mice in surveillance videos. Previously we took advantage of our developed algorithm and analyzed the objects of mating mice in the consecutive frames, we unprecedentedly showed that, to the best of our knowledge, the mice mating behavior can be automatically detected based on video processing (Lo et al., 2009 [13]). In this paper, we proposed an improved method which monitors the distance between two mating objects and more effectively detects the mating behavior. In addition, a more detailed portrayal of the mating behavior can be further elaborated as a function of the distance patterns in the tails of two caged mice. Experimental results show that the current system can effectively detect the mice mating behavior with the highest precision rate of 96.1%, far better than that of our previously proposed method.

[1]  Z. Kalafatic.  Model-based tracking of laboratory animals , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[2]  Thomas C. Henderson,et al.  Video-based Animal Behavior Analysis From Multiple Cameras , 2006, 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems.

[3]  Rong Zhang,et al.  A Multi-scale Phase Method for Content Based Image Retrieval , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[4]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[5]  Idaku Ishii,et al.  Real-time scratching behavior quantification system for laboratory mice using high-speed vision , 2009, Journal of Real-Time Image Processing.

[6]  T. Burghardt,et al.  Analysing animal behaviour in wildlife videos using face detection and tracking , 2006 .

[7]  Hsuan-Ting Chang,et al.  Efficient Moving Object Extraction in Compressed Low-Bit-Rate Video , 2006, 2006 International Conference on Intelligent Information Hiding and Multimedia.

[8]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[9]  Takeshi Takaki,et al.  High-speed video analysis of laboratory rats behaviors in forced swim test , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[10]  Hsuan-Ting Chang,et al.  Automatic detection of mouse mating behavior by video surveillance. , 2009 .

[11]  Idaku Ishii,et al.  Real-time and Long-time Quantification of Behavior of Laboratory Mice Scratching , 2007, 2007 IEEE International Conference on Automation Science and Engineering.

[12]  Pan Wei,et al.  A Background Reconstruction Method Based on Double-background , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[13]  Slobodan Ribaric,et al.  A system for tracking laboratory animals based on optical flow and active contours , 2001, Proceedings 11th International Conference on Image Analysis and Processing.

[14]  Liang-Gee Chen,et al.  Efficient moving object segmentation algorithm using background registration technique , 2002, IEEE Trans. Circuits Syst. Video Technol..

[15]  Hiroyuki Ishii,et al.  Development of autonomous experimental setup for behavior analysis of rats , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Jie Zhou,et al.  Adaptive background estimation for real-time traffic monitoring , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[17]  Idaku Ishii,et al.  Automatic Scratching Pattern Detection for Laboratory Mice Using High-Speed Video Images , 2008, IEEE Transactions on Automation Science and Engineering.

[18]  Thomas Serre,et al.  Automated home-cage behavioural phenotyping of mice. , 2010, Nature communications.

[19]  Paolo Dario,et al.  Development of a novel quadruped mobile robot for behavior analysis of rats , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Masatoshi Ishikawa,et al.  A Reconfigurable Embedded System for 1000 f/s Real-Time Vision , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Kai-Ming Chan,et al.  The use of motion analysis to measure pain-related behaviour in a rat model of degenerative tendon injuries , 2009, Journal of Neuroscience Methods.