Currently, I work on Object detection, tracking and counting and I want to store the result from object detection ,tracking, and counting and whenever the vehicle cross the line, the result always give me duplicate. how can i prevent that

and here for 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()