The Design And Implementation Of Intelligent Monitoring System Of Human Body

Intelligent human monitoring has always been an important and difficult problem in the computer vision area. As a problem that involves many research areas, it is a high-level pattern recognition application. This thesis focuses on solving two major problems of intelligent human monitoring:human identity verification, and human activity analysis. An intelligent human monitoring system is designed aiming at smart home application. Main contributes are listed as follows:Firstly, a solution about intelligent human monitoring framework is designed, with the main concern on human identity verification and human activity analysis. The former is solved with a random forest based fast human face detection algorithm. The later problem is departed to two levels. The HMM method is used to realize the simple human action recognition, and then the combination of the actions is used to deal with the high-level human activity analysis.Secondly, using Kinect sensor, the depth image is used to help converting2D images to images with3D information. With depth image, a3D human skeleton model is obtained to solve view-changing problem in2D image processing.For the face detection problem, using random forest, a fast algorithm is designed to detect and verify specific human face in real-time, and has the ability of online learning. The online learning enables the model to learn the feature of the specific human face in real-time, which improves the face detection performance and the adaption for the human face with multiple view-points. The experiment result shows the validation of the algorithm.For the human activity analysis problem,3D human skeleton model is used to extract features that can stand for human pose change, and the pose features are then combined with human motion features. The proposed features can represent the human actions well. Then HMM method is used to model and recognize human actions, which improves the stability of human action recognition well. After that, using the human action recognition result, a fuzzy rule-based human activity classification method is designed to realize the high-level human activity analysis. Experiment results show the effectiveness of the human action features, as well as the good performance of the action recognition and the activity analysis.