Developing Advanced Deep Learning Algorithms for Video-based Human Action Recognition

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Human action and behaviours recognition is essential for industries and healthcare applications. Some progress has been made on the analysis of static images, however, many behaviours cannot be recognised by still images because of the inability of capturing the temporal information of the behaviour motion. With the advances of science and technology and the convenience of people's life, video files have become easy to obtain. Video files record the time sequence of human behaviours and actions, the objects of occurrence, and the environment in which they occur. Recently, the deep learning-based method show great success in the video action recognition task. However, there are some considerable problems in applications, On the one hand, the variety of video viewpoints and video appearance, lead to a significant decrease in the accuracy in practical application. On the other hand, video processing involves a 3D network to process frames of images, which will generate a lot of parameters and computational overhead. In turn, this poses a problem to computing hardware in terms of speed and memory and an excessively large network is not suitable for practical application. Thus, this project will focus on the development of machine learning and high-performance computing methods for the accurate and effective recognition of human action and behaviour. Deep learning techniques will be investigated towards high recognition performance with smaller network architecture. New computing approaches will be studied to speed up the process via GPU. The models will be developed and validated by public and private datasets.