  Human activity recognition is one of the most important tasks in computer
vision and has proved useful in different fields such as healthcare, sports
training and security. There are a number of approaches that have been explored
to solve this task, some of them involving sensor data, and some involving
video data. In this paper, we aim to explore two deep learning-based
approaches, namely single frame Convolutional Neural Networks (CNNs) and
convolutional Long Short-Term Memory to recognise human actions from videos.
Using a convolutional neural networks-based method is advantageous as CNNs can
extract features automatically and Long Short-Term Memory networks are great
when it comes to working on sequence data such as video. The two models were
trained and evaluated on a benchmark action recognition dataset, UCF50, and
another dataset that was created for the experimentation. Though both models
exhibit good accuracies, the single frame CNN model outperforms the
Convolutional LSTM model by having an accuracy of 99.8% with the UCF50 dataset.
