- 期刊
Human Gait Classification Using Motion Information
Human Gait Classification Using Motion Information
並列摘要
In this paper, human gaits in the video streams were identified using the local motion features and the hidden Morkov model (HMM) method. First of all, the regions of multiple moving targets, called region of interest (ROI) are detected, labeled, and tracked from the image sequences in the clustering background. The local motion vectors relative to the ROI's center are used to represent human activities. These vectors are transferred to the sequence of states to form the training and testing samples using the clustering algorithms. Four activities, including walking, running, hopping, and limping, are specified and designed for identifying human activities in video streams. Some experimental results are illustrated to show the validity of the proposed methods. Finally, the conclusions and future works are given.