Papers › Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition
Hierarchical Long Short-Term Concurrent Memory for Human Interaction Recognition
Xiangbo Shu, Jinhui Tang, Guo-Jun Qi, Wei Liu, Jian Yang
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Group Activity Recognition | Collective Activity | H-LSTCM | Accuracy | 83.75 | #5 of 6 | Archive leaderboard | report |
| Group Activity Recognition | Volleyball | H-LSTCM | Accuracy | 88.4 | #11 of 12 | Archive leaderboard | report |
| Human Interaction Recognition | BIT | H-LSTCM | Accuracy | 94.03 | #1 of 3 | Archive leaderboard | report |
| Human Interaction Recognition | UT | H-LSTCM | Accuracy | 98.33 | #1 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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