def update(self, pred_dets, pred_embs):
        """
        Perform measurement update and track management.
        Args:
            pred_dets (np.array): Detection results of the image, the shape is
                [N, 6], means 'cls_id, score, x0, y0, x1, y1'.
            pred_embs (np.array): Embedding results of the image, the shape is
                [N, 128], usually pred_embs.shape[1] is a multiple of 128.
        """
        pred_cls_ids = pred_dets[:, 0:1]
        pred_scores = pred_dets[:, 1:2]
        pred_xyxys = pred_dets[:, 2:6]
        pred_tlwhs = np.concatenate((pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1), axis=1)

        detections = [
            Detection(tlwh, score, feat, cls_id)
            for tlwh, score, feat, cls_id in zip(pred_tlwhs, pred_scores,
                                                 pred_embs, pred_cls_ids)
        ]

        # Run matching cascade.
        matches, unmatched_tracks, unmatched_detections = \
            self._match(detections)

        # Update track set.
        for track_idx, detection_idx in matches:
            self.tracks[track_idx].update(self.motion,
                                          detections[detection_idx])
        for track_idx in unmatched_tracks:
            self.tracks[track_idx].mark_missed()
        for detection_idx in unmatched_detections:
            self._initiate_track(detections[detection_idx])
        self.tracks = [t for t in self.tracks if not t.is_deleted()]

        # Update distance metric.
        active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
        features, targets = [], []
        for track in self.tracks:
            if not track.is_confirmed():
                continue
            features += track.features
            targets += [track.track_id for _ in track.features]
            track.features = []
        self.metric.partial_fit(
            np.asarray(features), np.asarray(targets), active_targets)
        output_stracks = self.tracks
        return output_stracks

        Perform measurement update and track management.
        Args:
            pred_dets (np.array): Detection results of the image, the shape is
                [N, 6], means 'cls_id, score, x0, y0, x1, y1'.
            pred_embs (np.array): Embedding results of the image, the shape is
                [N, 128], usually pred_embs.shape[1] is a multiple of 128.
        
ppdet/modeling/mot/tracker/deepsort_tracker.py