class Tracklet:
STATUS_BORN = 0
STATUS_TRACKING = 1
STATUS_LOST = 2
def __init__(self, global_id, x, y, confidence, timestamp):
self.id = global_id
self.last_update = timestamp
self.estimator = KalmanFilter(x, y, 1 - confidence)
self.status = Tracklet.STATUS_BORN
def get_tracklet_info(self):
return {
'id': self.id,
'x': self.estimator.mu[0],
'y': self.estimator.mu[2],
'v': sqrt(self.estimator.mu[1]**2 + self.estimator.mu[3]**2),
'phi': atan2(self.estimator.mu[1], self.estimator.mu[3]),
'sigma_x': self.estimator.sigma[0][0],
'sigma_y': self.estimator.sigma[2][2],
'status': self.status
}
def estimate_position_at(self, timestamp):
estimate = self.estimator.estimate_at(timestamp - self.last_update)[0]
return estimate[0], estimate[2]
def predict(self, timestamp):
self.estimator.predict(math.fabs(self.last_update - timestamp))
self.last_update = timestamp
def update(self, detection, confidence):
self.estimator.correct(detection, 1 - confidence)
def similarity(self, coordinates, timestamp):
return euclidean(self.estimate_position_at(timestamp), coordinates)
def confidence(self):
return self.estimator.sigma[0][0] * self.estimator.sigma[2][2]
