Class BYTETracker

tutorials/qdtrack/tracker_reid_motion.py:165–345

class BYTETracker(object):
    def __init__(self, frame_rate=30):
        self.tracked_stracks = []  # type: list[STrack]
        self.lost_stracks = []  # type: list[STrack]
        self.removed_stracks = []  # type: list[STrack]

        self.frame_id = 0
        
        self.low_thresh = 0.2
        self.track_thresh = 0.8
        self.det_thresh = self.track_thresh + 0.1
        
        
        self.buffer_size = int(frame_rate / 30.0 * 30)
        self.max_time_lost = self.buffer_size
        self.kalman_filter = KalmanFilter()

#     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]