Articulated Human Pose Estimation

Mykhaylo Andriluka & Leonid Pishchulin

Articulated Human Pose Estimation

Human body pose contains a wealth of information about a person’s intention, attitude and internal state. The focus of our research is to estimate body pose in realistic conditions such as images and videos found on YouTube or captured with a mobile phone. We envision that developed approaches will become building blocks in such applications as activity recognition, markerless motion capture and augmented reality.

We build on the recent advances in hierarchical image representations with convolutional neural networks (CNN) and explore two novel research directions: joint estimation of poses of multiple people and 3D human pose estimation from only a few mobile cameras.

Detection and pose estimation in multi-person scenes

We propose an approach that jointly solves the tasks of articulated person detection and pose estimation. Our approach infers the number of persons in a scene, identifies occluded body parts, and disambiguates the body parts of different people in close proximity of each other [ see figure ]. Our formulation is based on partitioning and labeling a set of body part hypotheses generated with a CNN-based body part detector. The partitioning is accomplished by solving an integer-linear program that resembles correlation clustering approaches previously proposed for image and video segmentation. One of the advantages of our formulation is that it implicitly performs non-maximum suppression, removing spurious body part detections in the background and merging multiple correct detections corresponding to the same person. We evaluate our approach on standard benchmarks showing its advantages over previously proposed strategies that operated by first detecting the people and then independently estimating their body poses.