Automated Mouse Behavior Recognition using VGG Features and LSTM Networks
We present a mouse behavior classification method using a recurrent neural network with the long short term memory (LSTM) model. The experimental hardware used to collect the data is a custom mouse cage with four stereo camera pairs in each wall. Using as input the different videos, our computational method employs a so called end to end learning approach: visual features from pre trained convolutional neural networks are extracted from each image frame, and used to train a customized LSTM based model in weakly supervised fashion, to recognize different behaviors of the mouse in the videos. Future extensions of the system will incorporate 3D feature information from the stereo cameras and online classification functionality.