MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation

2014/09/28 by Arjun Jain, Jonathan Tompson, Jain, Arjun +5 · 10 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Video Analysis and Summarization #Video Surveillance and Tracking Methods #cs.CV #cs.LG #cs.NE

paper · pdf · doi:10.48550/arxiv.1409.7963

arxiv created 2014/09/28 · openalex publication_date 2014/09/28 · arxiv updated 2014/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.

Citations

• Visual perception of biological motion and a model for its analysis
• Visualizing and Understanding Convolutional Networks

Cited by

• FootFormer: Estimating Stability from Visual Input
• Thin-Slicing Network: A Deep Structured Model for Pose Estimation in Videos
• PoseTrack: Joint Multi-Person Pose Estimation and Tracking
• Recurrent Network Models for Human Dynamics
• Learning Spatiotemporal Features with 3D Convolutional Networks
• Bi-directional Graph Structure Information Model for Multi-Person Pose Estimation
• Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision
• Real-time Human Pose Estimation from Video with Convolutional Neural Networks
• EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras
• Human Pose and Path Estimation from Aerial Video Using Dynamic Classifier Selection

Related

• Learning Human Pose Estimation Features with Convolutional Networks
• Deep Learning-Based Human Pose Estimation: A Survey
• Real-time Human Pose Estimation from Video with Convolutional Neural Networks
• Deep Learning-based Human Pose Estimation: A Survey
• Learning to Estimate Pose by Watching Videos