  Humans are able to intuitively deduce actions that took place between two
states in observations via deductive reasoning. This is because the brain
operates on a bidirectional communication model, which has radically improved
the accuracy of recognition and prediction based on features connected to
previous experiences. During the past decade, deep learning models for action
recognition have significantly improved. However, deep neural networks struggle
with these tasks on a smaller dataset for specific Action Recognition (AR)
tasks. As with most action recognition tasks, the ambiguity of accurately
describing activities in spatial-temporal data is a drawback that can be
overcome by curating suitable datasets, including careful annotations and
preprocessing of video data for analyzing various recognition tasks. In this
study, we present a novel lightweight framework combining transfer learning
techniques with a Conv2D LSTM layer to extract features from the pre-trained
I3D model on the Kinetics dataset for a new AR task (Smart Baby Care) that
requires a smaller dataset and less computational resources. Furthermore, we
developed a benchmark dataset and an automated model that uses LSTM convolution
with I3D (ConvLSTM-I3D) for recognizing and predicting baby activities in a
smart baby room. Finally, we implemented video augmentation to improve model
performance on the smart baby care task. Compared to other benchmark models,
our experimental framework achieved better performance with less computational
resources.
