• Corpus ID: 37545044

Automated Mouse Behavior Recognition using VGG Features and LSTM Networks

@inproceedings{Kramida2016AutomatedMB,
  title={Automated Mouse Behavior Recognition using VGG Features and LSTM Networks},
  author={Gregory Kramida and Yiannis Aloimonos and Cornelia Ferm{\"u}ller and Nikolas A. Francis},
  year={2016}
}
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… 
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Recurrent 3D Convolutional Network for Rodent Behavior Recognition
  • Van Anh Le, K. Murari
  • Computer Science
    ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2019
TLDR
A deep architecture which is a combination of recurrent network and 3D convolutional network to learn long and short-term video representations is proposed, demonstrating that the framework can obtain accuracy on par with human assessment.
Context-Aware Mouse Behavior Recognition Using Hidden Markov Models
TLDR
This paper develops and implements a novel hidden Markov model (HMM) algorithm to describe the temporal characteristics of mouse behaviors, and proposes a hybrid deep learning architecture, where the first unsupervised layer relies on an advanced spatial-temporal segment Fisher vector encoding both visual and contextual features.
Deep learning improves automated rodent behavior recognition within a specific experimental setup
Semi-supervised sequence modeling for improved behavioral segmentation
TLDR
It is shown that a large number of unlabeled frames can improve supervised segmentation in the regime of sparse hand labels and also show that a small number of hand labeled frames can increase the precision of unsupervised segmentation.
Automated Recognition of Rodent Social Behavior
TLDR
It is demonstrated that placing the human in the annotation loop reduces the annotation time substantially compared to traditional, sequential labeling, and this interactive annotation approach enables neuroscientists to analyze behavioral data quicker than before and to study previous data in new light with limited manual work.
P.140 Automated video tracking and behavior recognition in rodents: deep learning improves tracking robustness and classification accuracy

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