OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
This provides a holistic perception framework for real-time applications like self-driving cars and delivery robots, though it is incremental in extending previous Composite Fields methods.
The paper tackles the problem of joint detection and spatio-temporal association of semantic keypoints, such as in pose estimation and tracking, by introducing a single-stage real-time framework using Composite Fields and Temporal Composite Association Fields (TCAF). It achieves competitive accuracy while being an order of magnitude faster on datasets like COCO, CrowdPose, and PoseTrack.
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.