The recognition of human action is widely applied in video surveillance, virtual reality and in some human-computer interaction areas such as user experience designing tasks. Pattern recognition becomes a hot topic in the field of computer vision. In this report, I summarize human behavior recognition problem as a problem of acquiring computing data through motion detection and symbolic acting information. Then extract and understand the behavior of the action features to achieve classification target. On this basis, I review the moving object detection, motion feature extraction and movement characteristics to understand the technical analysis, the correlation method classification, and discuss the difficulties and research directions of this project.
This project uses a public dataset called ’50 salad’. Assisted by a support vector machine, this project aims to design and establish a well working Hidden Markov probability model for the machine learning and pattern recognition operation. The results of this project can be used to improve pattern recognition model. Feature recognition and machine learning can be used to develop specific products to improve the lives of people with disabilities, manufacture productivity and so on.