DEEPSORT Problem

Hi, I trained a model by yolo to detecet objects on videos and tracking by deep_sort_realtime.deepsort_tracker. the detection accuracy is very well but id of one object by changing the frames change a lot. how can i solve it by giving a stable id to a object???

I used a lot of ReID models but i have got a lot of error. my code import cv2
import numpy as np
from ultralytics import YOLO
from deep_sort_realtime.deepsort_tracker import DeepSort

# Load the YOLO model by providing the path to the trained model's weights.
model = YOLO('/home/train2/weights/best.pt')

# Initialize the Deep SORT tracker.
deepsort = DeepSort(
max_age=40, # Maximum number of frames a track can be missing before it's deleted.
n_init=2, # Minimum number of consecutive detections to confirm a track.
nn_budget=None, # Number of features to keep for matching (None means unlimited).
)

# Open the video file using OpenCV's VideoCapture function.
video_path = '/home/pythonProject/s3.mp4'
cap = cv2.VideoCapture(video_path)

# Process each frame from the video.
while cap.isOpened():
ret, frame = cap.read() # Read a single frame from the video.
if not ret:
break # Break the loop if no frame is returned (end of video).

# Perform object detection on the current frame using the YOLO model.
results = model(frame, stream=True)

# Initialize a list to store detections in the current frame.
detections = []
for result in results:  # Loop over the detected objects in the current frame.
    for box in result.boxes.data.tolist():  # Get the bounding box for each detection.
        x1, y1, x2, y2, conf = box[:5]  # Extract the coordinates and confidence score.

        # Filter out detections with a confidence score lower than 0.5.
        if conf < 0.5:
            continue  # Skip low-confidence detections.

        # Prepare the bounding box in (x, y, width, height) format for Deep SORT.
        bbox = [x1, y1, x2 - x1, y2 - y1]

        # Append the detection (bounding box and confidence) to the list of detections.
        detections.append((np.array(bbox), conf))

# Update the Deep SORT tracker with the current frame's detections.
outputs = deepsort.update_tracks(detections, frame=frame)

# Process each track output from the tracker.
for track in outputs:
    track_id = track.track_id  # Get the unique ID for this track (assigned by Deep SORT).
    bbox = track.to_ltrb()  # Convert the bounding box to (left, top, right, bottom) format.
    x1, y1, x2, y2 = map(int, bbox)  # Convert the bounding box coordinates to integers.

    # Draw a bounding box for each tracked object.
    cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Green color for all tracked objects.
    cv2.putText(frame, f'ID: {track_id}', (x1, y1 - 10),
                cv2.FONT_HERSHEY_SIMPLEX, .51, (0, 255, 0), 2)

# Display the processed frame with tracking.
cv2.imshow('YOLO Movement Detection', frame)

# Exit the loop if the 'q' key is pressed.
if cv2.waitKey(1) & 0xFF == ord('q'):
    break

# Release the video capture object and close all OpenCV windows.
cap.release()
cv2.destroyAllWindows()