Monocular Human Depth Estimation Via Pose Estimation

Jinyoung Jun

doi:10.1109/ACCESS.2021.3126629

Monocular Human Depth Estimation Via Pose Estimation

2021-01-01

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DOI：10.1109/ACCESS.2021.3126629

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We propose a novel monocular depth estimator, which improves the prediction accuracy on human regions by utilizing pose information. The proposed algorithm consists of two networks - PoseNet and DepthNet - to estimate keypoint heatmaps and a depth map, respectively. We incorporate the pose information from PoseNet to improve the depth estimation performance of DepthNet. Specifically, we develop the feature blending block, which fuses the features from PoseNet and DepthNet and feeds them into the next layer of DepthNet, to make the networks learn to predict the depths of human regions more accurately. Furthermore, we develop a novel joint training scheme using partially labeled datasets, which balances multiple loss functions effectively by adjusting weights. Experimental results demonstrate that the proposed algorithm can improve depth estimation performance significantly, especially around human regions. For example, the proposed algorithm improves the depth estimation performance on the human regions of ResNet-50 by 2.8% and 7.0% in terms of delta 1 and RMSE, respectively, on the proposed HD + P dataset.

Keywords:

Monocular depth estimation

human pose estimation

human depth estimation

loss rebalancing strategy

Monocular Human Depth Estimation Via Pose Estimation

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