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On Multiscale Self-Similarities Description for Effective Three-Dimensional/Six-Dimensional Motion Trajectory Recognition

机译：有效三维/六维运动轨迹识别的多尺度自相似描述

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摘要

Motion trajectories provide compact informative clues in characterizing motion behaviors of human bodies, robots, and moving objects. This paper devises an invariant and unified descriptor for three-dimensional/six-dimensional (3-D/6-D) motion trajectories recognition by exploring the latent motion patterns in the multiscale self-similarity matrices (MSM) within a motion trajectory and its components. The MSM approach transforms a motion trajectory in Euclidean space into a set of similarity matrices and exhibits strong invariances, in which each matrix can be regarded as a grayscale image. Next, the histograms of oriented gradients features extracted from the MSM representation are concatenated as the final trajectory descriptor. In addition, an improved kernel MSM is raised by calculating the pairwise kernel distances. Finally, extensive 3-D/6-D motion trajectory recognition experiments on three public datasets with a linear support vector machine classifier are conducted to verify the effectiveness and efficiency of the proposed approach.
机译：运动轨迹为表征人体，机器人和运动物体的运动行为提供了紧凑的信息线索。通过探索运动轨迹内的多尺度自相似矩阵（MSM）中的潜伏运动模式及其运动，设计了一种不变的统一的三维/六维（3-D / 6-D）运动轨迹描述子。组件。 MSM方法将欧氏空间中的运动轨迹转换为一组相似性矩阵，并表现出很强的不变性，其中每个矩阵都可以视为灰度图像。接下来，将从MSM表示中提取的定向梯度特征直方图串联起来作为最终轨迹描述符。另外，通过计算成对的内核距离，提出了改进的内核MSM。最后，利用线性支持向量机分类器对三个公共数据集进行了广泛的3-D / 6-D运动轨迹识别实验，以验证该方法的有效性和效率。

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