Articulated body pose estimation

Articulated body pose estimation in computer vision is the study of algorithms and systems that recover the pose of an articulated body, which consists of joints and rigid parts using image-based observations. It is one of the longest-lasting problems in computer vision because of the complexity of the models that relate observation with pose, and because of the variety of situations in which it would be useful. Description Perception of human beings in their neighboring environment is an important capability that robots must possess. If a person uses gestures to point to a particular object, then the interacting machine should be able to understand the situation in real world context. Thus pose estimation is an important and challenging problem in computer vision, and many algorithms have been deployed in solving this problem over the last two decades. Many solutions involve training complex models with large data sets. Pose estimation is a difficult problem and an active

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Patient MoCap: Human Pose Estimation under Blanket Occlusion for Hospital Monitoring Applications

Huseyin Coskun, Alexandru Eugen Ichim

Motion analysis is typically used for a range of diagnostic procedures in the hospital. While automatic pose estimation from RGB-D input has entered the hospital in the domain of rehabilitation medicine and gait analysis, no such method is available for bed-ridden patients. However, patient pose estimation in the bed is required in several fields such as sleep laboratories, epilepsy monitoring and intensive care units. In this work, we propose a learning-based method that allows to automatically infer 3D patient pose from depth images. To this end we rely on a combination of convolutional neural network and recurrent neural network, which we train on a large database that covers a range of motions in the hospital bed. We compare to a state of the art pose estimation method which is trained on the same data and show the superior result of our method. Furthermore, we show that our method can estimate the joint positions under a simulated occluding blanket with an average joint error of 7.56 cm.

2016

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Using DeepLabCut for 3D markerless pose estimation across species and behaviors

An Chi Chen, Mackenzie Mathis, Alexander Mathis, Tanmay Nath

Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open-source toolbox called DeepLabCut that builds on a state-of-the-art human pose-estimation algorithm to allow a user to train a deep neural network with limited training data to precisely track user-defined features that match human labeling accuracy. Here, we provide an updated toolbox, developed as a Python package, that includes new features such as graphical user interfaces (GUIs), performance improvements, and active-learning-based network refinement. We provide a step-by-step procedure for using DeepLabCut that guides the user in creating a tailored, reusable analysis pipeline with a graphical processing unit (GPU) in 1–12 h (depending on frame size). Additionally, we provide Docker environments and Jupyter Notebooks that can be run on cloud resources such as Google Colaboratory.

2019

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Using DeepLabCut for 3D markerless pose estimation across species and behaviors

An Chi Chen, Mackenzie Mathis, Alexander Mathis, Tanmay Nath

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.

Neuroscience2018

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