Automated Recognition of Social Behavior in Rats: The Role of Feature Quality

We investigate how video-based recognition of rat social behavior is affected by the quality of the tracking data and the derived feature set. We look at the impact of two common tracking errors – animal misidentification and inaccurate localization of body parts. We further examine how the complexity of representing the articulated body in the features influences the recognition accuracy. Our analyses show that correct identification of the rats is required to accurately recognize their interactions. Precise localization of multiple body points is beneficial for recognizing interactions that are described by a distinct pose. Including pose features only leads to improvement if the tracking algorithm can provide that data reliably.

[1]  A. Pérez-Escudero,et al.  idTracker: tracking individuals in a group by automatic identification of unmarked animals , 2014, Nature Methods.

[2]  T. Ono,et al.  A 3D-Video-Based Computerized Analysis of Social and Sexual Interactions in Rats , 2013, PloS one.

[3]  A. Cressant,et al.  Computerized video analysis of social interactions in mice , 2012, Nature Methods.

[4]  Kristin Branson,et al.  JAABA: interactive machine learning for automatic annotation of animal behavior , 2013, Nature Methods.

[5]  Tsuyoshi Koide,et al.  A male-specific QTL for social interaction behavior in mice mapped with automated pattern detection by a hidden Markov model incorporated into newly developed freeware , 2014, Journal of Neuroscience Methods.

[6]  Pietro Perona,et al.  Social behavior recognition in continuous video , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  O. Feinerman,et al.  Automated long-term tracking and social behavioural phenotyping of animal colonies within a semi-natural environment , 2013, Nature Communications.

[8]  Vittorio Murino,et al.  Automatic Visual Tracking and Social Behaviour Analysis with Multiple Mice , 2013, PloS one.

[9]  F. Hamprecht,et al.  Detecting individual body parts improves mouse behavior classification , 2014 .

[10]  I. J. Pinter,et al.  Automated Classification of Rat Social Behavior , 2014 .

[11]  P. Perona,et al.  utomated multi-day tracking of marked mice for the analysis of ocial behaviour , 2013 .

[12]  C. Braak,et al.  An automated system for the recognition of various specific rat behaviours , 2013, Journal of Neuroscience Methods.