基于测量协方差离散Kalman滤波估计算法的视频跟踪

视频中人体跟踪存在复杂性，尤其是对复杂背景下的人体上、下肢区域进行识别与跟踪时，传统算法存在一些问题。本文在传统Kalman滤波跟踪算法基础上，提出一种基于可变测量协方差的离散Kalman滤波人体识别算法。通过初始化测量协方差，用递归的方法从新获取的观测数据中计算出新的测量协方差估计量，通过离散Kalman滤波器进行跟踪。在实际的视频图像中，表现出良好的跟踪效果，并且对上肢、下肢及整个人体的区分以及部位跟踪方面都有很好的表现。相对于传统的Kalman滤波算法，本算法没有丢失跟踪目标的现象，跟踪速度适中，与人体行进速度保持一致，基本为1.5 m/s，特别适用于对视频中的人体行为进行跟踪及分析处理。

Measurement covariance discrete Kalman filter estimation algorithm for video tracking

Due to the complexity of human motion tracking in video, especially in complex background, there exist some problems in the region of human lower extremity recognition and tracking algorithm. Based on traditional Kalman filtering, a discrete Kalman filter body recognition algorithm is proposed based on variable measurement covariance. By measuring the covariance initialization, new covariance estimators are calculated from the observation data with the recursive method, and tracked by the discrete Kalman filter. The method show good tracking effect and performance when applying to the video image in real, and to distinguishing and tracking parts of upper limb, lower limb of the human body. Compared with traditional Kalman filter algorithm, the proposed method does not lose the tracking targets, the tracking speed is moderate maintaining consistent to human walking speed, which basically is 1.5 m/s. This algorithm is especially suitable for the video tracking and analysis for human behavior.