Multi-activity 3D human motion recognition and tracking in composite motion model with synthesized transition bridges

作     者：Yu, Jialin Sun, Jifeng Liu, Shengqing Luo, Shasha 

作者机构：South China Univ Technol Sch Elect & Informat Engn Guangzhou 510640 Guangdong Peoples R China 

出 版 物：《MULTIMEDIA TOOLS AND APPLICATIONS》 (Multimedia Tools Appl)

年 卷 期：2018年第77卷第10期

页      面：12023-12055页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China Guangdong Province National Science Foundation of China 

主　　题：Composite motion model 3D human motion analysis Motion modeling Transition bridges 

摘      要：Recognizing and tracking multiple activities are all extremely challenging machine vision tasks due to diverse motion types included and high-dimensional (HD) state space. To overcome these difficulties, a novel generative model called composite motion model (CMM) is proposed. This model contains a set of independent, low-dimensional (LD), and activity-specific manifold models that effectively constrain the state search space for 3D human motion recognition and tracking. This separate modeling of activity-specific movements can not only allow each manifold model to be optimized in accordance with only its respective movement, but also improve the scalability of the models. For accurate tracking with our CMM, a particle filter (PF) method is thus employed and then the particles can be distributed in all manifold models at each time step. In addition, an efficient activity switching strategy is proposed to dominate the particle distribution on all LD manifolds. To diffuse the particles amongst manifold models and respond quickly to the sudden changes in the activity, a set of visually-reasonable and kinematically-realistic transition bridges are synthesized by using the good properties of LD latent space and HD observation space, which enables the inter-activity motions seem more natural and realistic. Finally, a pose hypothesis that can best interpret the visual observation is selected and then used to recognize the activity that is currently observed. Extensive experiments, via qualitative and quantitative analyses, verify the effectiveness and robustness of our proposed CMM in the tasks of multi-activity 3D human motion recognition and tracking.