Behavioral tracking gets real

A deep-learning-based software package called DeepLabCut rapidly and easily enables video-based motion tracking in any animal species. Such tracking technology is bound to revolutionize movement science and behavioral tracking in the laboratory and is also poised to find many applications in the real world.

[1]  Jia Deng,et al.  A large-scale hierarchical image database , 2009, CVPR 2009.

[2]  Pietro Perona,et al.  Automated image-based tracking and its application in ecology. , 2014, Trends in ecology & evolution.

[3]  Huosheng Hu,et al.  Human motion tracking for rehabilitation - A survey , 2008, Biomed. Signal Process. Control..

[4]  Chris Button,et al.  A Review of Vision-Based Motion Analysis in Sport , 2008, Sports medicine.

[5]  Nicholas A. Steinmetz,et al.  Spontaneous behaviors drive multidimensional, brain-wide activity , 2018, bioRxiv.

[6]  E. Muybridge.  The science of animal locomotion (zoopraxography) : an electro-photographic investigation of consecutive phases of animal movements / by Eadward Muybridge ; executed and published under the auspices of the University of Pennsylvania. , .

[7]  Kristin Branson,et al.  Cortex commands the performance of skilled movement , 2015, eLife.

[8]  Kevin M. Cury,et al.  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning , 2018, Nature Neuroscience.

[9]  G. Johansson.  Visual motion perception. , 1975, Scientific American.

[10]  Bernt Schiele,et al.  DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model , 2016, ECCV.

[11]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Mark H. Johnson,et al.  Developmental pathways to autism: A review of prospective studies of infants at risk , 2014, Neuroscience & Biobehavioral Reviews.

[13]  Jake K. Aggarwal,et al.  Computer Analysis of Moving Polygonal Images , 1975, IEEE Transactions on Computers.