We propose a novel semi-supervised, Multi-Level Sequential Generative
Adversarial Network (MLS-GAN) architecture for group activity recognition. In
contrast to previous works which utilise manually annotated individual human
action predictions, we allow the models to learn it's own internal
representations to discover pertinent sub-activities that aid the final group
activity recognition task. The generator is fed with person-level and
scene-level features that are mapped temporally through LSTM networks.
Action-based feature fusion is performed through novel gated fusion units that
are able to consider long-term dependencies, exploring the relationships among
all individual actions, to learn an intermediate representation or `action
code' for the current group activity. The network achieves its semi-supervised
behaviour by allowing it to perform group action classification together with
the adversarial real/fake validation. We perform extensive evaluations on
different architectural variants to demonstrate the importance of the proposed
architecture. Furthermore, we show that utilising both person-level and
scene-level features facilitates the group activity prediction better than
using only person-level features. Our proposed architecture outperforms current
state-of-the-art results for sports and pedestrian based classification tasks
on Volleyball and Collective Activity datasets, showing it's flexible nature
for effective learning of group activities.

提出一种新颖的半监督、多层级顺序生成对抗网络（MLS-GAN）架构来进行群体活动识别，该网络架构利用个人和场景等级的特征，通过 LSTMs 实现映射，通过新颖的门控融合单元进行基于动作的特征融合，学习当前群体活动的中间表示或 “动作代码”，并具有半监督行为，可实现群体动作分类，相较于其他结构表现出更优秀的效果。