  Multi-person pose estimation (MPPE), which aims to locate the key points for
all persons in the frames, is an active research branch of computer vision.
Variable human poses and complex scenes make MPPE dependent on local details
and global structures; their absence may cause key point feature misalignment.
In this case, high-order spatial interactions that can effectively link the
local and global information of features are particularly important. However,
most methods do not include spatial interactions. A few methods have low-order
spatial interactions, but achieving a good balance between accuracy and
complexity is challenging. To address the above problems, a dual-residual
spatial interaction network (DRSI-Net) for MPPE with high accuracy and low
complexity is proposed herein. Compared to other methods, DRSI-Net recursively
performs residual spatial information interactions on the neighbouring features
so that more useful spatial information can be retained and more similarities
can be obtained between shallow and deep extracted features. The channel and
spatial dual attention mechanism introduced in the multi-scale feature fusion
also helps the network to adaptively focus on features relevant to the target
key points and further refine the generated poses. Simultaneously, by
optimising the interactive channel dimensions and dividing the gradient flow,
the spatial interaction module is designed to be lightweight, thus reducing the
complexity of the network. According to the experimental results on the COCO
dataset, the proposed DRSI-Net outperforms other state-of-the-art methods in
accuracy and complexity.
