Graph convolution networks (GCNs) have achieved remarkable performance in
skeleton-based action recognition. However, existing previous GCN-based methods
have relied excessively on elaborate human body priors and constructed complex
feature aggregation mechanisms, which limits the generalizability of networks.
To solve these problems, we propose a novel Spatial Topology Gating Unit
(STGU), which is an MLP-based variant without extra priors, to capture the
co-occurrence topology features that encode the spatial dependency across all
joints. In STGU, to model the sample-specific and completely independent
point-wise topology attention, a new gate-based feature interaction mechanism
is introduced to activate the features point-to-point by the attention map
generated from the input. Based on the STGU, in this work, we propose the first
topology-aware MLP-based model, Ta-MLP, for skeleton-based action recognition.
In comparison with existing previous methods on three large-scale datasets,
Ta-MLP achieves competitive performance. In addition, Ta-MLP reduces the
parameters by up to 62.5% with favorable results. Compared with previous
state-of-the-art (SOAT) approaches, Ta-MLP pushes the frontier of real-time
action recognition. The code will be available at
this https URL

基于骨骼的动作识别中，提出了一种新颖的基于空间拓扑门控单元（STGU）的多层感知器（MLP）模型，通过捕捉编码所有关节之间空间依赖关系的共同出现的拓扑特征来实现对样本特定且完全独立的逐点拓扑注意力的建模，并在三个大规模数据集上取得了竞争性的性能，提高了 62.5％的参数效率，推动了实时动作识别的前沿。

基于拓扑感知的骨架动作识别的多层感知机

Topology-aware MLP for Skeleton-based Action Recognition

In this paper we present a three-stream algorithm for real-time action
recognition and a new dataset of handwash videos, with the intent of aligning
action recognition with real-world constraints to yield effective conclusions.
A three-stream fusion algorithm is proposed, which runs both accurately and
efficiently, in real-time even on low-powered systems such as a Raspberry Pi.
The cornerstone of the proposed algorithm is the incorporation of both spatial
and temporal information, as well as the information of the objects in a video
while using an efficient architecture, and Optical Flow computation to achieve
commendable results in real-time. The results achieved by this algorithm are
benchmarked on the UCF-101 as well as the HMDB-51 datasets, achieving an
accuracy of 92.7% and 64.9% respectively. An important point to note is that
the algorithm is novel in the aspect that it is also able to learn the
intricate differences between extremely similar actions, which would be
difficult even for the human eye. Additionally, noticing a dearth in the number
of datasets for the recognition of very similar or fine-grained actions, this
paper also introduces a new dataset that is made publicly available, the Hand
Wash Dataset with the intent of introducing a new benchmark for fine-grained
action recognition tasks in the future.

本文提出了一种三流算法用于实时动作识别，并提供了新的手部洗涤视屏数据集，该算法不仅高效准确，而且能够识别极其相似的动作，从而为未来的精细动作识别任务引入了新的基准。

精细动作的实时识别及其手洗数据集

Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset

Recognizing every person's action in a crowded and cluttered environment is a
challenging task. In this paper, we propose a real-time action recognition
method, Action4D, which gives reliable and accurate results in the real-world
settings. We propose to tackle the action recognition problem using a holistic
4D "scan" of a cluttered scene to include every detail about the people and
environment. Recognizing multiple people's actions in the cluttered 4D
representation is a new problem. In this paper, we propose novel methods to
solve this problem. We propose a new method to track people in 4D, which can
reliably detect and follow each person in real time. We propose a new deep
neural network, the Action4D-Net, to recognize the action of each tracked
person. The Action4D-Net's novel structure uses both the global feature and the
focused attention to achieve state-of-the-art result. Our real-time method is
invariant to camera view angles, resistant to clutter and able to handle crowd.
The experimental results show that the proposed method is fast, reliable and
accurate. Our method paves the way to action recognition in the real-world
applications and is ready to be deployed to enable smart homes, smart factories
and smart stores.

本文提出了一种名为 Action4D 的实时行动识别方法，它使用全局特征和聚焦注意力相结合的创新结构进行行动识别，可在复杂的环境中进行精确而可靠的多人行动识别。该方法能够灵活适应不同的场景，对视角及影响行动识别的杂乱因素均有良好的抵抗能力，从而为实现智能家居、智能工厂和智能商店等各种智能应用提供了奠基性的方法。