This page display the submited results for Pose Leaderboards. For each submission, we display several main metrics in main table. For detailed information, more metrics, per-sequence results and visualisation (coming soon), please click submission name. For all tables, you can click headers to sort the results. Note you can download the submission zip file as well. Legends, metrics descriptions and reference are displayed after leaderboards table. For more information on submission preparation, click here.

2D Pose Estimation Submissions

Name OSPAIoU AP0.5

0.303 64.047%
Jiajun Fu, Yonghao Dang, Ruoqi Yin, Shaojie Zhang, Feng Zhou, Wending Zhao, Jianqin Yin An Improved Baseline Framework for Pose Estimation Challenge at ECCV 2022 Visual Perception for Navi in Arxiv

0.45 43.342%
Anonymous Submission

0.459 42.338%
Anonymous Submission

Additional Information Used

Symbol Description
Individual Image Method uses individual images from each camera
Stitched Image Method uses stitched images combined from the individual cameras
Pointcloud Method uses 3D pointcloud data
Online Tracking Method does frame-by-frame processing with no lookahead
Offline Tracking Method does not do in-order frame processing
Public Detections Method uses publicly available detections
Private Detections Method uses its own private detections

Evaluation Measures [1]

Measure Better Perfect Description
OSPAPose lower 0.0 OSPA-Pose is a pose estimation metric which directly captures the distance between two sets of poses without a thresholding parameter.
AP0.5 higher 100% Average Precision of pose estimation with mean OKS above 0.5 [4].


  1. The style and content of the Evaluation Measures section is reference from MOT Challenges.
  2. Hamid Rezatofighi∗, Tran Thien Dat Nguyen∗, Ba-Ngu Vo, Ba-Tuong Vo, Silvio Savarese, and Ian Reid. How Trustworthy are Performance Evaluations for Basic Vision Tasks? Arxiv, 2021.