Abstract
AbstractThe ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external hardware based on posture. Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. Here we provide a new DeepLabCut-Live! package that achieves low-latency real-time pose estimation (within 15 ms, >100 FPS), with an additional forward-prediction module that achieves zero-latency feedback, and a dynamic-cropping mode that allows for higher inference speeds. We also provide three options for using this tool with ease: (1) a stand-alone GUI (called DLC-Live! GUI), and integration into (2) Bonsai and (3) AutoPilot. Lastly, we benchmarked performance on a wide range of systems so that experimentalists can easily decide what hardware is required for their needs.
Publisher
Cold Spring Harbor Laboratory
Reference53 articles.
1. Abadi M , Barham P , Chen J , Chen Z , Davis A , Dean J , Devin M , Ghemawat S , Irving G , Isard M , et al. Tensorflow: A system for large-scale machine learning. In: 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16); 2016. p. 265–283.
2. Alted F , Haenel V , Team BD , Python-Blosc; 2020. http://github.com/blosc/python-blosc.
3. Andriluka M , Iqbal U , Insafutdinov E , Pishchulin L , Milan A , Gall J , Schiele B . Posetrack: A benchmark for human pose estimation and tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. p. 5167–5176.
4. Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio
5. Blazeface: Sub-millisecond neural face detection on mobile gpus;arXiv preprint,2019
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献