Human Action Recognition from Silhouettes Using Manifold Learning and MDA
In this paper,a method for human action recognition based on silhouette observations is presented.The methodology combines supervised Neighborhood Preserving Embedding (NPE) based dimensionality reduction and Multiple Discriminant Analysis (MDA) based action discrimination.Since depth is more robust to appearance variations and enables the spatial relationship to be explored easily for future multi-target interaction,a miniature stereo vision machine with three cameras is employed for generating high-resolution dense depth maps at video rate.The silhouettes of a moving person are extracted as the input features through fusing color and depth information.Motivated by the observation that human activities are often located on a low-dimensional latent space,supervised NPE is adopted to learn the intrinsic action manifold.When the class information is available,MDA is utilized to find a linear subspace which is optimal for discrimination.Experimental results show that our system can recognize human actions accurately with temporal,intra-and inter-person variations.
action recognition manifold learning Linear Discriminant Analysis.
Fawang Liu Hongbin Deng
School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,CHINA
国际会议
International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)
上海
英文
2008-06-29（万方平台首次上网日期，不代表论文的发表时间）