Human activity recognition (HAR) is a key challenge in pervasive computing
and its solutions have been presented based on various disciplines.
Specifically, for HAR in a smart space without privacy and accessibility
issues, data streams generated by deployed pervasive sensors are leveraged. In
this paper, we focus on a group activity by which a group of users perform a
collaborative task without user identification and propose an efficient group
activity recognition scheme which extracts causality patterns from pervasive
sensor event sequences generated by a group of users to support as good
recognition accuracy as the state-of-the-art graphical model. To filter out
irrelevant noise events from a given data stream, a set of rules is leveraged
to highlight causally related events. Then, a pattern-tree algorithm extracts
frequent causal patterns by means of a growing tree structure. Based on the
extracted patterns, a weighted sum-based pattern matching algorithm computes
the likelihoods of stored group activities to the given test event sequence by
means of matched event pattern counts for group activity recognition. We
evaluate the proposed scheme using the data collected from our testbed and
CASAS datasets where users perform their tasks on a daily basis and validate
its effectiveness in a real environment. Experiment results show that the
proposed scheme performs higher recognition accuracy and with a small amount of
runtime overhead than the existing schemes.

在没有隐私和可访问性问题的智能空间中，基于部署的普适传感器生成的数据流被利用来实现一种高效的群体活动识别方案，该方案从一组用户生成的普适传感器事件序列中提取因果模式，以支持与最先进的图形模型相似的识别准确性。

一种面向群组活动识别的因果感知模式挖掘方案

A Causality-Aware Pattern Mining Scheme for Group Activity Recognition  in a Pervasive Sensor Space

Modeling relation between actors is important for recognizing group activity
in a multi-person scene. This paper aims at learning discriminative relation
between actors efficiently using deep models. To this end, we propose to build
a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture
the appearance and position relation between actors. Thanks to the Graph
Convolutional Network, the connections in ARG could be automatically learned
from group activity videos in an end-to-end manner, and the inference on ARG
could be efficiently performed with standard matrix operations. Furthermore, in
practice, we come up with two variants to sparsify ARG for more effective
modeling in videos: spatially localized ARG and temporal randomized ARG. We
perform extensive experiments on two standard group activity recognition
datasets: the Volleyball dataset and the Collective Activity dataset, where
state-of-the-art performance is achieved on both datasets. We also visualize
the learned actor graphs and relation features, which demonstrate that the
proposed ARG is able to capture the discriminative relation information for
group activity recognition.

本文提出了一种基于深度学习和 Actor Relation Graph（ARG）的方法，用于高效地学习和建模多人场景中演员之间的关系，并在两个标准数据集上取得了现有最佳表现

学习演员关系图用于群体活动识别

Learning Actor Relation Graphs for Group Activity Recognition

A group of persons can be analyzed at various semantic levels such as
individual actions, their interactions, and the activity of the entire group.
In this paper, we propose a structural recurrent neural network (SRNN) that
uses a series of interconnected RNNs to jointly capture the actions of
individuals, their interactions, as well as the group activity. While previous
structural recurrent neural networks assumed that the number of nodes and edges
is constant, we use a grid pooling layer to address the fact that the number of
individuals in a group can vary. We evaluate two variants of the structural
recurrent neural network on the Volleyball Dataset.

本文提出结构递归神经网络 (SRNN)，用于捕捉个体动作、个体间互动、以及整个群体活动。相比于之前的方法，本文使用网格池化层来解决群体中的个体数量变化的问题，同时在排球数据集上评估了两种 SRNN 变体的性能。