Prevent duplicate results from previous frame in object tracking

Question

Currently, I am working on Object detection, tracking, and counting. I want to store the results whenever the vehicle crosses the line, however, I always get duplicate results.

How can I prevent these duplicates from registering from a previous frame?

Here is the camera code:

class Camera(BaseCamera):
    """
    OpenCV video stream
    """
    video_source = 0
    start, end = Point(0, 500), Point(1280, 500)
    detector = Detector()
    tracker = ByteTrack()
    line_zone = LineZone(start=start, end=end)
    annotator = LineZoneAnnotator()

    def __init__(self, enable_detection: bool = False):
        video_source = os.environ.get("VIDEO_SOURCE")
        try:
            video_source = int(video_source)
        except Exception as exp:    # pylint: disable=broad-except
            if not video_source:
                raise EnvironmentError("Cannot open the video source!") from exp
        finally:
            Camera.set_video_source(video_source)
        super().__init__()
        self.enable_detection = enable_detection

    @staticmethod
    def set_video_source(source):
        """Set video source"""
        Camera.video_source = source

    @classmethod
    def frames(cls):
        """
        Get video frame
        """
        camera = cv2.VideoCapture(Camera.video_source)
        if not camera.isOpened():
            raise RuntimeError("Could not start camera.")

        while True:
            # read current frame
            ret, img = camera.read()

            # Loop back
            if not ret:
                camera.set(cv2.CAP_PROP_POS_FRAMES, 0)
                continue

            # Object detection
            results = cls.detector(image=img)
            selected_classes = [ 2, 3]

            tensorflow_results = results.detections
            cls.annotator.annotate(img, cls.line_zone)
            if not tensorflow_results:
                yield cv2.imencode(".jpg", img)[1].tobytes()
                continue

            detections = Detections.from_tensorflow(tensorflow_results=tensorflow_results)

            detections = cls.tracker.update_with_detections(detections=detections)
            detections = detections[np.isin(detections.class_id, selected_classes)]
            
            result=cls.line_zone.trigger(detections)
            if type(result)!=type(None) and len(result)>=3:

                print(result[2])
                
            img = visualize(image=img, detections=detections)

            # encode as a jpeg image and return it
            yield cv2.imencode(".jpg", img)[1].tobytes()