Towards a kinect-based behavior recognition and analysis system for small animals

This paper presents a Microsoft Kinect^®-based image processing system that is capable of automated tracking and behavior recognition in freely moving animals. The depth image provided by the Kinect infrared (IR) camera is used in the image processing algorithm, which works under both bright and dark conditions, compared to conventional red-green-blue (RGB) cameras that need proper lighting or LEDs on the headstage. For animal tracking, the subject trajectory was recorded/refreshed every 0.5 s, with a maximum positioning error of 1.6 cm. For behavior recognition, 5 different types of rodent behavior were considered: standstill, walking, grooming, rearing, and rotating are classified using a support vector machine (SVM) with radial basis function kernels. The algorithm was verified in vivo using data acquired from a 2 month-old Sprague Dawley rat weighting ~400 grams in a standard homecage and compared with manual ground truth. The overall behavior recognition accuracy was 95.34% and 89.41% in bright and dark conditions, respectively.