A brief introduction to the SORT tracking algorithm and its Python implementation example

SORT (Simple Online and Realtime Tracking) is a target tracking algorithm based on Kalman filter, which can robustly track moving targets in real-time scenes. The SORT algorithm was originally proposed by Alex Bewley and others in 2016. It has been widely used in various applications in the field of computer vision, such as video surveillance, autonomous driving, robot navigation, etc.

The SORT algorithm is mainly based on two core ideas: Kalman filtering and Hungarian algorithm. Kalman filter is an algorithm for estimating system state. It can use the dynamic model of the system and sensor measurements to predict and update the system state, thereby improving the accuracy of state estimation. The Hungarian algorithm is an algorithm used to solve the maximum weight matching problem in bipartite graphs. It can find the maximum weight matching given a bipartite graph.

The main steps of the SORT algorithm are as follows:

Target detection: Use target detection algorithms (such as YOLO, SSD, etc.) to extract the objects in the current frame target information.

State prediction: For each tracked target, use Kalman filter to predict its state.

Data association: Based on the prediction status and the target information in the current frame, use the Hungarian algorithm to perform data association to find the target corresponding to each tracked target in the current frame.

Status update: For each tracked target, use Kalman filter to update its status.

Target output: Output the status information and tracking results of each tracked target.

In computer vision, the SORT algorithm can be applied to various target tracking scenarios. For example, in video surveillance, the SORT algorithm can track moving targets in real time, thereby enabling detection and early warning of abnormal behaviors in the scene. In the field of autonomous driving, the SORT algorithm can track other vehicles, pedestrians and other traffic participants to achieve autonomous navigation and obstacle avoidance of vehicles. In robot navigation, the SORT algorithm can track moving targets to achieve autonomous navigation and obstacle avoidance of the robot.

The following is a simple sample code implemented in 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]

The above code implements a simple SORT tracking algorithm, using the Kalman filter to calculate the target position and velocity Make predictions and estimates, then use the Hungarian algorithm to associate the target, and finally determine whether the target is dead based on the number of consecutive invisible times of the target and remove the dead target. The above code implements a simple SORT tracking algorithm, using the Kalman filter to predict and estimate the target position and speed, then using the Hungarian algorithm to associate the target, and finally judging whether the target is dead and removing the death based on the number of consecutive invisible times of the target. The goal.

In addition to the SORT algorithm, there are many other target tracking algorithms, such as Kalman filter, particle filter, multi-target tracking, etc. Each algorithm has its applicable scenarios, advantages and disadvantages. In practical applications, it is necessary to select an appropriate algorithm for target tracking based on specific scenarios and needs.

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