OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

03/03/2021 ∙ by Sven Kreiss, et al. ∙ 7

Many image-based perception tasks can be formulated as detecting, associating and tracking semantic keypoints, e.g., human body pose estimation and tracking. In this work, we present a general framework that jointly detects and forms spatio-temporal keypoint associations in a single stage, making this the first real-time pose detection and tracking algorithm. We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e.g., a person's body joints) in multiple frames. For the temporal associations, we introduce the Temporal Composite Association Field (TCAF) which requires an extended network architecture and training method beyond previous Composite Fields. Our experiments show competitive accuracy while being an order of magnitude faster on multiple publicly available datasets such as COCO, CrowdPose and the PoseTrack 2017 and 2018 datasets. We also show that our method generalizes to any class of semantic keypoints such as car and animal parts to provide a holistic perception framework that is well suited for urban mobility such as self-driving cars and delivery robots.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 3

page 5

page 6

page 8

page 9

page 10

page 13

Code Repositories

openpifpaf

Official implementation of "OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association" in PyTorch.


view repo

openpifpaf

Official implementation of "PifPaf: Composite Fields for Human Pose Estimation" in PyTorch.


view repo

openpifpafwebdemo

Web browser based demo of OpenPifPaf.


view repo

openpifpafwebdemo

Web browser based demo of OpenPifPaf.


view repo

openpifpaf_posetrack

OpenPifPaf plugin for Posetrack


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.