Flexible trajectory indexing for 3D motion recognition

Motion trajectory analysis is important for human motion recognition and human computer interaction. In this paper, we propose a flexible 3D trajectory indexing method for complex 3D motion recognition. Based on both point level and primitive-level descriptors, trajectories are represented in the sub-primitive level, the level between the point level and primitive level. Primitives are flexibly segmented into sub-primitives in various scales, and the sub-primitives retain more detailed information than primitives. The detailed level of sub-primitives can be adjusted by controlling segmentation scales according to motion complexities. The proposed approach is suitable for spatial motion trajectory, which is view-invariant in 3D space. A cluster model is also proposed to represent motion classes and motion recognition performed based on maximum a posteriori (MAP) criterion. The experiments on benchmark datasets validate the effectiveness of the proposed approach.