class_docstr: Implementation of the SMOT tracker
  The steps to use the tracker is:
  0. Set anchors from the SSD
  1. First call tracker.predict(new_frame)
  2. Then get the tracking anchor information
  3. Run the detractor with the tracking anchor information
  4. Run tracker.update(new_detection, track_info).
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code: def _produce_frame_result(self):
      tracked_objects = []
      for track in self.active_tracks:
          box = {
              "left": track.mean[0],
              "top": track.mean[1],
              "width": track.mean[2] - track.mean[0],
              "height": track.mean[3] - track.mean[1],
          }
          tid = track.display_id
          age = track.age
          classId = track.class_id
          obj = {
              "bbox": box,
              "track_id": tid,
              "age": age,
              "class_id": classId,
              "class_name": self.coco_class2index_dict[classId],
          }
          if track.attributes is not None:
              obj["landmarks"] = track.attributes
          if obj["class_name"] in self.class_set:
              tracked_objects.append(obj)
      return tracked_objects
  
imports: from __future__ import absolute_import
  import logging
  import time
  import numpy
  import mxnet
  from gluoncv.utils.bbox import bbox_iou
  from gluoncv.data import COCODetection
  from .utils import timeit, Track
  from .motion_estimation import FarneBeckFlowMotionEstimator
  from .motion_estimation import DummyMotionEstimator
label_desc: Use image node_id 9 for calling the SMOTTracker obj's underlying member method code with example usage: obj._produce_frame_result() and returns: tracked_objects