改进隐马氏模型的运动人体模型学习(英文)

Kinetic people model learning of modified HMM

基于人体模型的跟踪方法易于实现视频的运动人体跟踪,而且利用较少的视频帧数即可学习获得人体模型。本文针对给出的视频提出了学习人体模型的学习算法。利用片图模型表示未经学习的人体,改进的隐马尔可夫模型( HMM)模拟人体在视频序列各帧间的运动,并使用机器学习方法对该改进的HMM进行推理,获取改进HMM的参数,从而获得所需的人体模型。学习得到的人体模型由包含颜色信息的各人体肢体模板组成。实验显示只用80～90帧包含有人体运动的序列图像,便可学习得到该运动人体的人体模型。结果表明,该学习框架效果明显,可用于快速学习视频序列中的运动人体模型,且可用于学习一人或多人的人体模型。

The tracking method based on a people model contributes to realizing the kinetic people tracking for a given video,and it can learn the people model by using much less frames of the video. This paper proposes a learn algorithm for learning the people model in the given video. By using a tree pictorial structure model to represent the detuned generic people in the video, and a modified Hidden Markov Model（HMM） to simulate the motion of people between the two frames of the video, a machine learning method is used to the modified HMM to obtain the estimation of parameter of the modified HMM,and to capture the people model from the video. The learned model consists of different body templates covered with color information. For learning the color of the local parts of the people model by proposed algorithm, an instance-specific model has been obtained. The experiment demonstrates that the kinetic people by proposed algorithm model can be learned with sequences images of 80 -90 frames involving people motion,which shows the learning method works well for learning the kinetic people model based on video, and can rapidly learn people models for one or more persons in the video.