Under the influence of novel corona virus pneumonia epidemic prevention and control, higher requirements for behavior recognition in complex environment are put forward. The accuracy of traditional methods for sports training is not high, so a method is needed to improve the local action recognition to assist sports training. In the process of behavior recognition, if only the track is regarded as an independent individual, the information of its neighbor will be ignored. Therefore, we use KNN algorithm to get the nearest neighbor trajectory. In order to calculate the rich neighborhood information around the track, this paper calculates the complex relationship between the center track and the neighborhood track from four different angles, including absolute motion, relative motion, distance relationship and direction relationship. Then, from the four different perspectives of variance, discrete coefficient, skewness and kurtosis, this paper proposes a large interval nearest neighbor coding method. This method makes the four eigenvalues complement each other and improves the ability of describing complex and changeable behaviors. The experimental results show that the coding method proposed in this paper can be used for behavior recognition according to different transformation matrix.

中文翻译：

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基于轨道时空特征的行为识别

在新型冠状病毒性肺炎的流行防治中，对复杂环境下的行为识别提出了更高的要求。传统运动训练方法的准确性不高，因此需要一种改善局部动作识别的方法来辅助运动训练。在行为识别过程中，如果仅将轨道视为独立的个体，则将忽略其邻居的信息。因此，我们使用KNN算法来获取最近的邻居轨迹。为了计算轨道周围的丰富邻域信息，本文从绝对运动，相对运动，距离关系和方向关系四个角度计算了中心轨道与邻域轨道之间的复杂关系。然后，从方差，离散系数，偏度和峰度四个不同角度，提出了一种大区间最近邻编码方法。该方法使四个特征值相互补充，并提高了描述复杂和可变行为的能力。实验结果表明，本文提出的编码方法可以根据不同的变换矩阵用于行为识别。