DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning

Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animal's body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit,DeepPoseKit, that addresses these problems using an efficient multi-scale deep-learning model, calledStacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed>2 × with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings-including groups of interacting individuals. Our work reduces barriers to using advanced tools fo r measuring behavior and has broad applicability across the behavioral sciences.
Source: eLife - Category: Biomedical Science Tags: Neuroscience Source Type: research