Temporal flow maps for limbs (Tml, LFM)

• Won the Honorable Mention Award in the POSETRACK challenge at ECCV 2018 workshop.
• Ranked 4th in the Multi-person pose tracking. (named as MIPAL)
• We propose a pose estimator and tracker based on TML(LFM: Limbflow map) which is designed to represent a temporal movement of a person by estimating the direction of limbs’ movement.

• Our paper was accepted at IJCNN 2019.

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.