Learning a projective mapping to locate animals in video using RFID

We present a method to locate animals in video based on their reported positions using noisy and biased measurements from a radio frequency identification (RFID) system. The system uses a kernel regression method to learn a mapping from reported X, Y, Z locations in the environment to X, Y pixel locations in video with minimal calibration and training data. Our goal is for this system to facilitate animal behavior research by enabling automatic identification of interactions between animals and then providing the location of the animals in video so that the details of each interaction can be examined more closely by either humans or machines. The primary contribution of this work is achieving efficient and reliable 3D to 2D projective mapping in a non-parametric way while also overcoming challenges that would otherwise affect accuracy. Our system successfully addresses issues regarding noisy positional data, position bias, occlusion of RFID tags, and wide angle lens distortion. We validate the system experimentally indoors as well as in the field and compare the accuracy of our system with the standard camera projection model-based procedure.