Robust mouse tracking in complex environments using neural networks
Year: 2019
Type: article
Abstract: Both the training data and trained models used in the paper are found here. Dataset description Information for each dataset falls into 3 folders. Filenames portray the dataset split used in the paper that they belong to (eg Training_1.png or Validation_1.png). Ref/*.png: Input image (image before annotation) Seg/*.png: Segmentation image. Values = 0 are background. Values > 0 are mouse. Ell/*.txt: Ellipse-fit data. Data is tab-delimited as follows: X Center of Ellipse (px) Y Center of Ellipse (px) Minor Axis Length of Ellipse (px) Major Axis Length of Ellipse (px) Angle Direction (Degrees). 0 is down with + values going counter-clockwise. Trained Model Description We also release models trained on all the subsets of training data we share. Each trained model was trained using our code over on Github: https://github.com/KumarLabJax/MouseTracking Brief descriptions of the training subsets Please read the associated paper for additional detail. A brief summary of the environment is added here: Standard Open Field Strain Survey We annotated 16234 training and 568 validation images of a single mouse in the same open field. The mouse can be one of multiple coat colors, but visually appears as a black, light-grey, or white color. In the case the mouse’s posture created a poor ellipse-fit, portions of the mouse were removed (such as tail) to enable a good ellipse-fit. 24Hr Open Field Dataset We annotated 2099 training and 93 validation images of a single mouse in the same open field listed above augmented with bedding and a food container. All mice in this experiment appear black on video. There are 2 states, with visible light and with only infrared. The infrared-only imaging contains much higher visual noise. KOMP Open Field Dataset We annotated 1000 training and 83 validation images of a single mouse in JAX’s KOMP2 open field arena. All mice have a black coat color. Test Ground Truth Dataset To test the robustness of our system against conventional trackers that build a background model from multiple frames in a video, we re-sampled video a 20 minute video at 1 frame per second and annotated all the resulting frames (1179-1200 frames). We did this for the 6 environments in the paper of varying difficulty (Black, Gray, Piebald, Albino, 24Hr, KOMP2). The format of this data follows a DataSubset_FrameNumber format instead of Training/Validation_FrameNumber format.
Authors: Geuther, Brian Q., Deats, Sean P., Fox, Kai J., Murray, Steve A., Braun, Robert E., White, Jacqueline K., Chesler, Elissa J., Lutz, Cathleen M., Kumar, Vivek
Institution: Jackson Laboratory
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6968736807