多对象的卡尔曼滤波器：识别问题

Question

我想使用我所附的卡尔曼滤波器实现来跟踪视频中流动的物体.在视频中，物体是运动的，有的从视频的边界出来，有的进入视频.算法不是跟踪新的元素，而是将轨道从旧的物体移动到新的物体上.如何解决这个问题？

这里有一个例图如果我说得不够清楚的话，我还想再解释一下。

class KalmanFilter(object):


def __init__(self):
    self.dt = 0.005  # delta time

    self.A = np.array([[1, 0], [0, 1]])  # matrix in observation equations
    self.u = np.zeros((2, 1))  # previous state vector

    # (x,y) tracking object center
    self.b = np.array([[0], [255]])  # vector of observations

    self.P = np.diag((3.0, 3.0))  # covariance matrix
    self.F = np.array([[1.0, self.dt], [0.0, 1.0]])  # state transition mat

    self.Q = np.eye(self.u.shape[0])  # process noise matrix
    self.R = np.eye(self.b.shape[0])  # observation noise matrix
    self.lastResult = np.array([[0], [255]])

def predict(self):
    # Predicted state estimate
    self.u = np.round(np.dot(self.F, self.u))
    # Predicted estimate covariance
    self.P = np.dot(self.F, np.dot(self.P, self.F.T)) + self.Q
    self.lastResult = self.u  # same last predicted result
    return self.u

def correct(self, b, flag):
    if not flag:  # update using prediction
        self.b = self.lastResult
    else:  # update using detection
        self.b = b
    C = np.dot(self.A, np.dot(self.P, self.A.T)) + self.R
    K = np.dot(self.P, np.dot(self.A.T, np.linalg.inv(C)))

    self.u = np.round(self.u + np.dot(K, (self.b - np.dot(self.A,
                                                          self.u))))
    self.P = self.P - np.dot(K, np.dot(C, K.T))
    self.lastResult = self.u
    return self.u