  Understanding accurate information on human behaviours is one of the most
important tasks in machine intelligence. Human Activity Recognition that aims
to understand human activities from a video is a challenging task due to
various problems including background, camera motion and dataset variations.
This paper proposes two CNN based architectures with three streams which allow
the model to exploit the dataset under different settings. The three pathways
are differentiated in frame rates. The single pathway, operates at a single
frame rate captures spatial information, the slow pathway operates at low frame
rates captures the spatial information and the fast pathway operates at high
frame rates that capture fine temporal information. Post CNN encoders, we add
bidirectional LSTM and attention heads respectively to capture the context and
temporal features. By experimenting with various algorithms on UCF-101,
Kinetics-600 and AVA dataset, we observe that the proposed models achieve
state-of-art performance for human action recognition task.
