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In this work we propose an online multi person pose tracking approach whichworks on two consecutive frames $I_{t-1}$ and $I_t$. The general formulation ofour temporal network allows to rely on any multi person pose estimationapproach as spatial network. From the spatial network we extract image featuresand pose features for both frames. These features serve as input for ourtemporal model that predicts Temporal Flow Fields (TFF). These TFF are vectorfields which indicate the direction in which each body joint is going to movefrom frame $I_{t-1}$ to frame $I_t$. This novel representation allows toformulate a similarity measure of detected joints. These similarities are usedas binary potentials in a bipartite graph optimization problem in order toperform tracking of multiple poses. We show that these TFF can be learned by arelative small CNN network whilst achieving state-of-the-art multi person posetracking results.

Joint Flow: Temporal Flow Fields for Multi Person Tracking