Opencv ip camera or video is very slow

I am using cv2 with yolo as well as paddle ocr to recognize license plates directly from the camera, however it is extremely slow and laggy, I wish it could be smoother, although it cannot be achieved in real time, but at least it only delays at an acceptable level (less than 1s). I have read the solutions in stack overflow, opencv forum but have not found a suitable answer, please help me, because if I cannot complete this project, the company will not let me step into AI projects Later, this was considered for me to prove that I had the ability to participate in AI in the future. Here is the code I use:
`def tracking_camera(source, model= YOLO(“./model/”)):

track_history = defaultdict(lambda: [])
# lấy thông tin về source
w, h, fps = (int(source.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('output_test_8.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))

# Loop through the video frames
while source.isOpened():
    # Read a frame from the video
    success, frame =

    if success:
        # Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)
        #gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        # Visualize the results on the frame
        annotated_frame = results[0].plot(labels= False, boxes=False)

        if results[0] != None:
            boxes = results[0].boxes.xywh.cpu().numpy().astype(int)
            bboxes = results[0].boxes.xyxy.cpu().numpy().astype(int)
            track_ids = results[0]
            confidences = results[0].boxes.conf.cpu().numpy().astype(int)

        # Plot the tracks
            for box, track_id, bbox in zip(boxes, track_ids, bboxes):
                x, y, w, h = box
                x1, y1, x2, y2= bbox
                track = track_history[track_id]
                track.append((float(x), float(y)))  # x, y center point
                if len(track) > 30:  # retain 90 tracks for 90 frames

                #get license plate
                gray_frame= frame[int(y1):int(y2), int(x1):int(x2)]
                gray_frame = cv2.cvtColor(gray_frame, cv2.COLOR_BGR2GRAY)
                _,_, text= get_license_plate(gray_frame)

                # Draw the tracking lines
                points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
                cv2.polylines(annotated_frame, [points], isClosed=False, color=(51,51,102), thickness=4)

                # Draw bounding box without displaying confidence
                annotated_frame= check_license_plate_and_display_information(text,annotated_frame)
                cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
                cv2.putText(annotated_frame, f'#{track_id} {text}', (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 255, 0), 4)

                cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), (0, 255, 0), 2)

        # Display the annotated frame
        cv2.imshow("YOLOv8 Tracking", annotated_frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
        # Break the loop if the end of the video is reached


This is the result I want to display on the screen. If there is no information, it will display unknown. If there is information, it will display the name and image.

Are you trying to detect or track?

but besides that, note that you are tracking, displaying annotations and writing frames in a single thread. So first try to write every task as a separate function and then measure the timing of each task to check the performance bottlenecks.

I’m looking to detect and track at the same time, if it’s too slow then I’ll prioritize detect and stop tracking. I see the problem is that there are too many operations in the loop, I just want to optimize it or multi-threaded, asynchronous operations,… but I don’t have this knowledge and if I learn it myself I won’t know Which direction is right for me? It will take a lot of time for me to find the right and optimal direction. So I hope to receive advice and help from people who have done it and encountered this problem like me, or similar