Deep Learning for Human Action Recognition

摘要

The aim of this project is to develop a model for human actions such as running, jogging, walking, clapping, hand-waving and boxing. A series of videos is given for the layout, where an individual executes an event in each video. The action performed on that particular video will be the label of a video. This relationship must be learned by the model, and the label of an input (video) which he never saw can then be predicted. Technically, despite descriptions of these acts, the model would need to learn to distinguish between various human behaviors. There may be many content identification programs which can work on following jobs like Active object tracking for identifying an item such as a vehicle or a human from a CCTV picture and learning the patterns in the movement of humans when we are able to create a pattern that will guide us (humans) to perform a variety of activities.