SORT(Simple Online and Realtime Tracking)? Kalman ?? ??? ?? ?? ??????, ??? ???? ???? ??? ???? ??? ? ????. SORT ????? ?? Alex Bewley ?? 2016?? ??????. ?? ??, ?? ??, ?? ????? ?? ?? ??? ?? ??? ??? ?? ???? ?? ???????.

SORT ????? ?? ?? ???? ???? ??????? ? ?? ?? ????? ???? ???. ?? ??? ??? ??? ???? ?? ??????, ???? ?? ??? ?? ??? ???? ??? ??? ???? ???????? ?? ??? ???? ???? ? ????. ??? ????? ?? ????? ?? ??? ?? ??? ???? ? ???? ???????. ? ????? ??? ?? ????? ?? ??? ??? ?? ? ????.

SORT ????? ?? ??? ??? ????.

?? ??: ?? ?? ????(?: YOLO, SSD ?)? ???? ?? ????? ?? ??? ?????.

?? ??: ??? ? ??? ?? Kalman ??? ???? ??? ?????.

??? ??: ?? ???? ?? ??? ?? ??? ???? ??? ????? ???? ??? ??? ???? ?? ????? ??? ? ??? ???? ??? ????.

?? ????: ??? ? ??? ?? Kalman ??? ???? ??? ???????.

?? ??: ??? ? ??? ?? ??? ?? ??? ?????.

??? ???? SORT ????? ??? ?? ?? ????? ??? ? ????. ?? ??, ??? ???? SORT ????? ???? ??? ????? ??? ? ???? ??? ????? ??? ???? ?? ??? ? ????. ?? ?? ???? SORT ????? ?? ??, ??? ? ?? ?? ???? ???? ??? ?? ?? ? ??? ??? ??? ? ????. ?? ???? SORT ????? ???? ??? ???? ??? ?? ?? ? ??? ??? ??? ? ????.

??? Python?? ??? ??? ?? ?????.

#python
import numpy as np
from filterpy.kalman import KalmanFilter
from scipy.optimize import linear_sum_assignment

class Track:

def init(self,prediction,track_id,track_lifetime):
    self.prediction=np.atleast_2d(prediction)
    self.track_id=track_id
    self.track_lifetime=track_lifetime
    self.age=0
    self.total_visible_count=1
    self.consecutive_invisible_count=0

def predict(self, kf):
    self.prediction = kf.predict()
    self.age += 1

def update(self, detection, kf):
    self.prediction = kf.update(detection)
    self.total_visible_count += 1
    self.consecutive_invisible_count = 0

def mark_missed(self):
    self.consecutive_invisible_count += 1

def is_dead(self):
    return self.consecutive_invisible_count >= self.track_lifetime

class Tracker:

def init(self,track_lifetime,detection_variance,process_variance):
    self.next_track_id=0
    self.tracks=[]
    self.track_lifetime=track_lifetime
    self.detection_variance=detection_variance
    self.process_variance=process_variance
    self.kf=KalmanFilter(dim_x=4,dim_z=2)
    self.kf.F=np.array([[1,0,1,0],
                    [0,1,0,1],
                    [0,0,1,0],
                    [0,0,0,1]])
    self.kf.H=np.array([[1,0,0,0],
                    [0,1,0,0]])
    self.kf.R=np.array([[self.detection_variance,0],
                    [0,self.detection_variance]])
    self.kf.Q=np.array([[self.process_variance,0,0,0],
                    [0,self.process_variance,0,0],
                    [0,0,self.process_variance,0],
                    [0,0,0,self.process_variance]])

def update(self, detections):
    # predict track positions using Kalman filter
    for track in self.tracks:
        track.predict(self.kf)

    # associate detections with tracks using Hungarian algorithm
    if len(detections) > 0:
        num_tracks = len(self.tracks)
        num_detections = len(detections)
        cost_matrix = np.zeros((num_tracks, num_detections))
        for i, track in enumerate(self.tracks):
            for j, detection in enumerate(detections):
                diff = track.prediction - detection
                distance = np.sqrt(diff[0,0]**2 + diff[0,1]**2)
                cost_matrix[i,j] = distance
        row_indices, col_indices = linear_sum_assignment(cost_matrix)
        unassigned_tracks = set(range(num_tracks)) - set(row_indices)
        unassigned_detections = set(range(num_detections)) - set(col_indices)
        for i, j in zip(row_indices, col_indices):
            self.tracks[i].update(detections[j], self.kf)
        for i in unassigned_tracks:
            self.tracks[i].mark_missed()
        for j in unassigned_detections:
            new_track = Track(detections[j], self.next_track_id, self.track_lifetime)
            self.tracks.append(new_track)
            self.next_track_id += 1

    # remove dead tracks
    self.tracks = [track for track in self.tracks if not track.is_dead()]

    # return list of track positions
    return [track.prediction.tolist()[0] for track in self.tracks]

? ??? ??? SORT ?? ????? ?????. Kalman ??? ???? ?? ??? ??? ???? ??? ?? ??? ????? ???? ?????. ??? ????, ??? ?? ??? ?? ??? ???? ??? ?? ??? ????? ???? ?? ??? ?????. ?? ??? ??? SORT ?? ????? ?????, ?? ??? ???? ??? ??? ??? ???? ??? ?? ??? ????? ???? ??? ????? ????? ??? ?? ??? ???? ? ??? ???? ??? ?????. ??? ??? ??? ?? ??.

SORT ???? ??? ?? ??, ?? ??, ?? ?? ?? ? ??? ?? ?? ????? ????. ? ?????? ?? ??? ????, ?? ? ??? ????. ?? ????? ?? ????? ?? ??? ???? ?? ??? ??? ????? ???? ???.

? ??? SORT ?? ????? Python ?? ?? ?? ??? ??? ?? ?????. ??? ??? PHP ??? ????? ?? ?? ??? ?????!