python卡尔曼滤波_OpenCV3-Python卡尔曼(Kalman)滤波器介绍
卡尔曼滤波器主要由Rudolf Kalam在20世纪50年代末提出来的，它在众多领域得到应用，特别是交通工具的导航系统上经常用到。该方法认为物体的运动模型服从高斯模型，通过对输入数据流进行递归操作，预测目标的运动状态，然后通过与观察模型进行对比，根据误差来更新运动目标的状态。
举例说明：
假设桌子上有一个小红球，现场也有摄像头对着这一个场景。小球确定为被追踪的目标，用手指轻弹下小球，它会在桌子上滚动。如果球在一个特定方向上以1米/秒的速度滚动，则不需要卡尔曼滤波器也可估计1秒后球在哪里，因为1秒后它就在1米远处，利用卡尔曼滤波器可以基于上一帧的信息，预测目标在当前视频帧中的位置。
卡尔曼滤波算法分为两个阶段：
<1> 预测：在此阶段，卡尔曼滤波器使用由当前点计算的协方差来估计目标的新位置；
<2> 更新：在此阶段，卡尔曼滤波器记录目标的位置，并为下一次循环计算修正协方差；
1. 应用举例
绘制一个空帧和两条线：一条对应于鼠标的实际运动；另一条对应于卡尔曼滤波器预测的轨迹。import cv2, numpy as np
measurements = []
predictions = []
#创建800*800的空帧
frame = np.zeros((800, 800, 3), np.uint8)
#初始化测量数组和鼠标运动预测数组
last_measurement = current_measurement = np.array((2,1), np.float32)
last_prediction = current_prediction = np.zeros((2,1), np.float32)
#鼠标移动回调函数，绘制跟踪结果
def mousemove(event, x, y, s, p):
global frame, current_measurement, measurements, last_measurement, current_prediction, last_prediction
last_prediction = current_prediction
last_measurement = current_measurement
current_measurement = np.array([[np.float32(x)],[np.float32(y)]])
#Updates the predicted state from the measurement.
kalman.correct(current_measurement)
#Computes a predicted state.
current_prediction = kalman.predict()
lmx, lmy = last_measurement[0], last_measurement[1]
cmx, cmy = current_measurement[0], current_measurement[1]
lpx, lpy = last_prediction[0], last_prediction[1]
cpx, cpy = current_prediction[0], current_prediction[1]
cv2.line(frame, (lmx, lmy), (cmx, cmy), (0,100,0))
cv2.line(frame, (lpx, lpy), (cpx, cpy), (0,0,200))
cv2.namedWindow("kalman_tracker")
cv2.setMouseCallback("kalman_tracker", mousemove);
'''
cv2.KalmanFilter([dynamParams, measureParams[, controlParams[, type]]]) →
Parameters:
dynamParams – Dimensionality of the state.
measureParams – Dimensionality of the measurement.
controlParams – Dimensionality of the control vector.
type – Type of the created matrices that should be CV_32F or CV_64F.
The class implements a standard Kalman filter http://en.wikipedia.org/wiki/Kalman_filter, [Welch95]. However, you can modify transitionMatrix, controlMatrix, and measurementMatrix to get an extended Kalman filter functionality.
See the OpenCV sample kalman.cpp .
'''
#创建卡尔曼滤波器
kalman = cv2.KalmanFilter(4,2,1)
kalman.measurementMatrix = np.array([[1,0,0,0],[0,1,0,0]],np.float32)
kalman.transitionMatrix = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],np.float32)
kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03
while True:
cv2.imshow("kalman_tracker", frame)
if cv2.waitKey(100) & 0xff == ord("q"):
break
if cv2.waitKey(100) & 0xff == ord("q"):
cv2.imwrite('kalman.jpg', frame)
break
cv2.destroyAllWindows()
2. 实验结果
由于卡尔曼滤波算法的精度不是特高，因此，实际应用中，将其和CAMShift()算法相结合来获得更高的准确度和性能。
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