  In this paper, we aim to address the problem of human interaction recognition
in videos by exploring the long-term inter-related dynamics among multiple
persons. Recently, Long Short-Term Memory (LSTM) has become a popular choice to
model individual dynamic for single-person action recognition due to its
ability of capturing the temporal motion information in a range. However,
existing RNN models focus only on capturing the dynamics of human interaction
by simply combining all dynamics of individuals or modeling them as a whole.
Such models neglect the inter-related dynamics of how human interactions change
over time. To this end, we propose a novel Hierarchical Long Short-Term
Concurrent Memory (H-LSTCM) to model the long-term inter-related dynamics among
a group of persons for recognizing the human interactions. Specifically, we
first feed each person's static features into a Single-Person LSTM to learn the
single-person dynamic. Subsequently, the outputs of all Single-Person LSTM
units are fed into a novel Concurrent LSTM (Co-LSTM) unit, which mainly
consists of multiple sub-memory units, a new cell gate and a new co-memory
cell. In a Co-LSTM unit, each sub-memory unit stores individual motion
information, while this Co-LSTM unit selectively integrates and stores
inter-related motion information between multiple interacting persons from
multiple sub-memory units via the cell gate and co-memory cell, respectively.
Extensive experiments on four public datasets validate the effectiveness of the
proposed H-LSTCM by comparing against baseline and state-of-the-art methods.
