Human Pose Estimation 101

If you want a slightly more rigorous tutorial and understand the basics of Human Pose Estimation and how the field has evolved, check out these articles I published on 2D Pose Estimation and 3D Pose Estimation

Table of Contents

• Basics
• Loss
• Evaluation metrics
  • PCP
  • PCK
  • PDJ
  • MPJPE
  • AUC
• Important applications
• Extra

Basics

• Defined as the problem of localization of human joints (or) keypoints
• A rigid body consists of joints and rigid parts. A body with strong articulation is a body with strong contortion.
• Pose Estimation is the search for a specific pose in space of all articulated poses
• Number of keypoints varies with dataset - LSP has 14, MPII has 16, 16 are used in Human3.6m
• Classifed into 2D and 3D Pose Estimation
  • 2D Pose Estimation
  • Estimate a 2D pose (x,y) coordinates for each joint in pixel space from a RGB image
  • 3D Pose Estimation
  • Estimate a 3D pose (x,y,z) coordinates in metric space from a RGB image, or in previous works, data from a RGB-D sensor. (However, research in the past few years is heavily focussed on generating 3D poses from 2D images / 2D videos)

Loss

• Most commonly used loss function - Mean Squared Error, MSE(Least Squares Loss)
• This is a regression problem. The model will try to regress to the the correct coordinates, i.e move to the ground truth coordinatate’s in small increments. The model is trained to output continuous coordinates using a Mean Squared Error loss function

Evaluation metrics

Percentage of Correct Parts - PCP

• A limb is considered detected and a correct part if the distance between the two predicted joint locations and the true limb joint locations is at most half of the limb length (PCP at 0.5 )
• Measures detection rate of limbs
• Cons - penalizes shorter limbs
• Calculation
  • For a specific part, PCP = (No. of correct parts for entire dataset) / (No. of total parts for entire dataset)
  • Take a dataset with 10 images and 1 pose per image. Each pose has 8 parts - ( upper arm, lower arm, upper leg, lower leg ) x2
  • No of upper arms = 10 * 2 = 20
  • No of lower arms = 20
  • No of lower legs = No of upper legs = 20
  • If upper arm is detected correct for 17 out of the 20 upper arms i.e 17 ( 10 right arms and 7 left) → PCP = 17/20 = 85%
• Higher the better

Percentage of Correct Key-points - PCK

• Detected joint is considered correct if the distance between the predicted and the true joint is within a certain threshold (threshold varies)
• [email protected] is when the threshold = 50% of the head bone link
• [email protected] == Distance between predicted and true joint < 0.2 * torso diameter
• Sometimes 150 mm is taken as the threshold
• Head, shoulder, Elbow, Wrist, Hip, Knee, Ankle → Keypoints
• PCK is used for 2D and 3D (PCK3D)
• Higher the better

Percentage of Detected Joints - PDJ

• Detected joint is considered correct if the distance between the predicted and the true joint is within a certain fraction of the torso diameter
• Alleviates the shorter limb problem since shorter limbs have smaller torsos
• PDJ at 0.2 → Distance between predicted and true join < 0.2 * torso diameter
• Typically used for 2D Pose Estimation
• Higher the better

Mean Per Joint Position Error - MPJPE

• Per joint position error = Euclidean distance between ground truth and prediction for a joint
• Mean per joint position error = Mean of per joint position error for all k joints (Typically, k = 16)
• Calculated after aligning the root joints (typically the pelvis) of the estimated and groundtruth 3D pose.
• PA MPJPE
  • Procrustes analysis MPJPE.
  • MPJPE calculated after the estimated 3D pose is aligned to the groundtruth by the Procrustes method
  • Procrustes method is simply a similarity transformation
• Lower the better
• Used for 3D Pose Estimation

AUC

Important Applications

• Activity Analysis
• Human-Computer Interaction (HCI)
• Virtual Reality
• Augmented Reality
• Amazon Go presents an important domain for the application of Human Pose Estimation. Cameras track and recognize people and their actions, for which Pose Estimation is an important component. Entities relying on services that track and measure human activities rely heavily on human Pose Estimation

Informative roadmap on 2D Human Pose Estimation research

• Presentation by Wei Yang