Human Pose and Action Recognition

Anipose: a toolkit for robust markerless 3D pose estimation

Anipose provides a toolkit for markerless 3D pose estimation using 2D video, enabling analysis of animal movement and motor control.

B-SOiD: An Open Source Unsupervised Algorithm for Discovery of Spontaneous Behaviors

B-SOiD is an open-source unsupervised algorithm that identifies spontaneous behaviors from pose data, improving processing speed and generalization across subjects.

Multiple Monkey Pose Estimation Using OpenPose

This paper develops a monkey pose estimation system using OpenPose, achieving performance comparable to human models and running in real-time.

MacaquePose: A novel 'in the wild' macaque monkey pose dataset for markerless motion capture

The paper introduces MacaquePose, a dataset for markerless motion capture of macaques, showing neural network performance comparable to human-level pose estimation.

Real-time, low-latency closed-loop feedback using markerless posture tracking

This paper introduces a real-time, low-latency pose estimation tool with feedback capabilities for behavioral neuroscience experiments.

Deep-learning based identification, tracking, pose estimation, and behavior classification of interacting primates and mice in complex environments

This paper introduces a deep learning pipeline for tracking and classifying behaviors of interacting primates and mice in complex environments using video data.

OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

OpenMonkeyStudio provides a deep learning-based system for markerless 3D pose estimation in freely moving macaques, enabling automatic tracking and action recognition in unconstrained environments.

Performance Evaluation of Markerless 3D Skeleton Pose Estimates with Pop Dance Motion Sequence

This paper evaluates OpenPose's markerless 3D skeleton pose estimates against marker-based motion capture data for pop dance movements, showing average magnitude errors under 700 mm despite self-occlusions.

Evaluation of 3D markerless motion capture accuracy using OpenPose with multiple video cameras

This study evaluates the accuracy of 3D markerless motion capture using OpenPose with multiple cameras, showing 47% of errors below 20 mm and 80% below 30 mm, but some errors exceed 40 mm due to tracking failures.

FreiPose: A Deep Learning Framework for Precise Animal Motion Capture in 3D Spaces

FreiPose is a deep learning framework for precise 3D motion capture of animals, showing high accuracy and reliability in tracking rats during electrophysiological and optogenetic experiments.

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

The paper updates and provides a step-by-step guide for using DeepLabCut, an open-source toolbox for markerless 3D pose estimation across species and behaviors.