Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition
Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamics for single-person action recognition. However, existing RNN models only focus on capturing the temporal dynamics of the person-person interactions by naively combining the activity dynamics of individuals or modeling them as a whole. This neglects the inter-related dynamics of how person-person interactions change over time. To this end, we propose a novel Concurrent Long Short-Term Memories (Co-LSTM) to model the long-term inter-related dynamics between two interacting people on the bonding boxes covering people. Specifically, for each frame, two sub-memory units store individual motion information, while a concurrent LSTM unit selectively integrates and stores inter-related motion information between interacting people from these two sub-memory units via a new co-memory cell. In experiments, we show the superior performance of Co-LSTM compared with the state-of-the-arts methods.