Pose estimator and tracker using temporal flow maps for limbs
arXiv:1905.09500
Abstract
For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flow maps are unit vectors describing the limbs' movements. We constructed a network to learn both spatial information and temporal information end-to-end. Spatial information such as joint heatmaps and part affinity fields is regressed in the spatial network part, and the TML is regressed in the temporal network part. We also propose a data augmentation method to learn various types of TML better. The proposed multi-stride method expands the data by randomly selecting two frames within a defined range. We demonstrate that the proposed method efficiently estimates and tracks human poses on the PoseTrack 2017 and 2018 datasets.
Won the Honorable Mention Award in the 18'ECCV PoseTrack challenge. Accepted in the 19'IJCNN conference
References in corpus (9)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Caffe: Convolutional Architecture for Fast Feature Embedding
- MOT16: A Benchmark for Multi-Object Tracking
- Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
- Associative Embedding: End-to-End Learning for Joint Detection and Grouping
- Human pose estimation via Convolutional Part Heatmap Regression
- Stacked Hourglass Networks for Human Pose Estimation
- Joint Flow: Temporal Flow Fields for Multi Person Tracking
- Dual Path Networks for Multi-Person Human Pose Estimation