Method update

tutorials/qdtrack/tracker_reid_motion.py:183–345

(self, det_bboxes, det_labels, frame_id, track_feats)

#     def update(self, output_results):
    def update(self, det_bboxes, det_labels, frame_id, track_feats):

#         self.frame_id += 1
        self.frame_id = frame_id + 1
        activated_starcks = []
        refind_stracks = []
        lost_stracks = []
        removed_stracks = []
        
#         scores = output_results[:, 4]
#         bboxes = output_results[:, :4]  # x1y1x2y2
        scores = det_bboxes[:, 4].cpu().numpy()
        bboxes = det_bboxes[:, :4].cpu().numpy()

        track_feature = F.normalize(track_feats).cpu().numpy()

        remain_inds = scores > self.track_thresh
        dets = bboxes[remain_inds]
        scores_keep = scores[remain_inds]
        id_feature = track_feature[remain_inds]
        
        
        inds_low = scores > self.low_thresh
        inds_high = scores < self.track_thresh
        inds_second = np.logical_and(inds_low, inds_high)
        dets_second = bboxes[inds_second]
        scores_second = scores[inds_second]
        id_feature_second = track_feature[inds_second]
        
        if len(dets) > 0:
            '''Detections'''
            detections = [STrack(STrack.tlbr_to_tlwh(tlbr), s, f) for
                          (tlbr, s, f) in zip(dets, scores_keep, id_feature)]
        else:
            detections = []


        ''' Add newly detected tracklets to tracked_stracks'''
        unconfirmed = []
        tracked_stracks = []  # type: list[STrack]
        for track in self.tracked_stracks:
            if not track.is_activated:
                unconfirmed.append(track)
            else:
                tracked_stracks.append(track)

        ''' Step 2: First association, with Kalman and IOU'''
        strack_pool = joint_stracks(tracked_stracks, self.lost_stracks)
        # Predict the current location with KF
        STrack.multi_predict(strack_pool)
        
        dists = matching.embedding_distance(strack_pool, detections)
        dists = matching.fuse_motion(self.kalman_filter, dists, strack_pool, detections)
        matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.6)
#         dists = matching.iou_distance(strack_pool, detections)
#         matches, u_track, u_detection = matching.linear_assignment(dists, thresh=0.8)

        for itracked, idet in matches: