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

开发用于基于视频的人类动作识别的高级深度学习算法

• 批准号: 2640147
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2640147
• 关键词: Developing; Advanced; Deep; Learning; Algorithms

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.

人类行为和行为识别对于工业和医疗保健应用至关重要。静态图像的分析已经取得了一些进展，然而，由于无法捕获行为运动的时间信息，许多行为无法通过静态图像来识别。随着科技的进步和人们生活的便利，视频文件已经变得很容易获得。视频文件记录了人类行为和动作的时间顺序、发生的对象以及发生的环境。最近，基于深度学习的方法在视频动作识别任务中取得了巨大成功。然而，在应用中也存在一些相当大的问题，一方面，视频视点和视频外观的多样性，导致实际应用中的精度大幅下降。另一方面，视频处理涉及3D网络来处理图像帧，这会产生大量参数和计算开销。反过来，这给计算硬件带来了速度和内存方面的问题，过大的网络不适合实际应用。因此，该项目将专注于开发机器学习和高性能计算方法，以准确有效地识别人类的行为和行为。将研究深度学习技术，以实现较小网络架构的高识别性能。我们将研究新的计算方法，以通过 GPU 加速这一过程。这些模型将由公共和私人数据集开发和验证。